Add files using upload-large-folder tool

results/simvla_allfredf_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_allfredf_25_inner1_proj_type_relu_linear_ffn_type_relu_mlp_ffn_decoder_num_blocks_2-M20000-F10000-D10000/dataset_statistics.json

@@ -0,0 +1,2810 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  },
+  "handover_mic_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.3111798167228699,
+        0.9980528354644775,
+        0.5811634659767151,
+        0.23248183727264404,
+        0.1577642261981964,
+        -0.023892972618341446,
+        0.607450008392334,
+        0.37389707565307617,
+        1.1807876825332642,
+        0.6784368753433228,
+        0.25683099031448364,
+        -0.1075415387749672,
+        0.08219857513904572,
+        0.5450003743171692
+      ],
+      "std": [
+        0.2626854181289673,
+        0.7778211832046509,
+        0.46585971117019653,
+        0.5015608668327332,
+        0.2322310358285904,
+        0.10976110398769379,
+        0.4736306667327881,
+        0.2574075758457184,
+        0.7576491832733154,
+        0.4595378041267395,
+        0.5935487747192383,
+        0.16007103025913239,
+        0.14593657851219177,
+        0.4819530248641968
+      ],
+      "max": [
+        0.0,
+        2.1762497425079346,
+        1.5628178119659424,
+        0.990909993648529,
+        0.8559104800224304,
+        0.11719957739114761,
+        1.0,
+        0.837027370929718,
+        2.206538200378418,
+        1.6232832670211792,
+        1.1574815511703491,
+        0.2886713147163391,
+        0.5811867117881775,
+        1.0
+      ],
+      "min": [
+        -0.8862171173095703,
+        0.0,
+        0.0,
+        -1.0514172315597534,
+        -0.39507749676704407,
+        -0.4001063406467438,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0771245956420898,
+        -0.8672545552253723,
+        -0.07416637241840363,
+        0.0
+      ],
+      "q01": [
+        -0.7671213400363922,
+        0.0,
+        0.0,
+        -0.9784595704078675,
+        -0.27155110239982605,
+        -0.3553581619262695,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0119924187660216,
+        -0.651338517665863,
+        -0.06671902537345886,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.131481647491455,
+        1.404119849205017,
+        0.9854996800422668,
+        0.7671356797218323,
+        0.1121677927672863,
+        1.0,
+        0.7132604718208313,
+        2.139058828353882,
+        1.4547646927833575,
+        1.1053520989418035,
+        0.1410233974456787,
+        0.4914003610610962,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.30907687544822693,
+        0.9918185472488403,
+        0.5777602791786194,
+        0.22962018847465515,
+        0.15603166818618774,
+        -0.02409629337489605,
+        0.610062837600708,
+        0.37142202258110046,
+        1.1745105981826782,
+        0.6752545237541199,
+        0.2535216212272644,
+        -0.10635410249233246,
+        0.08214741945266724,
+        0.546892523765564
+      ],
+      "std": [
+        0.26320481300354004,
+        0.7802274823188782,
+        0.4670981466770172,
+        0.5009189248085022,
+        0.23124119639396667,
+        0.10963220149278641,
+        0.47306305170059204,
+        0.2583112120628357,
+        0.7615904211997986,
+        0.4614890217781067,
+        0.5927368402481079,
+        0.15948225557804108,
+        0.14593760669231415,
+        0.4817669689655304
+      ],
+      "max": [
+        0.0,
+        2.1762497425079346,
+        1.5628178119659424,
+        0.990909993648529,
+        0.8559104800224304,
+        0.11719957739114761,
+        1.0,
+        0.837027370929718,
+        2.206538200378418,
+        1.6232832670211792,
+        1.1574815511703491,
+        0.2886713147163391,
+        0.5811867117881775,
+        1.0
+      ],
+      "min": [
+        -0.8862171173095703,
+        0.0,
+        0.0,
+        -1.0514172315597534,
+        -0.39507749676704407,
+        -0.4001063406467438,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0771245956420898,
+        -0.8672545552253723,
+        -0.07416637241840363,
+        0.0
+      ],
+      "q01": [
+        -0.7671213400363922,
+        0.0,
+        0.0,
+        -0.9784595704078675,
+        -0.27155110239982605,
+        -0.3553581619262695,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0119924187660216,
+        -0.651338517665863,
+        -0.06671902537345886,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.131481647491455,
+        1.404119849205017,
+        0.9854996800422668,
+        0.7671356797218323,
+        0.11196876823902131,
+        1.0,
+        0.7132604718208313,
+        2.139058828353882,
+        1.4547646927833575,
+        1.0914406299591126,
+        0.1410233974456787,
+        0.4914003610610962,
+        1.0
+      ]
+    },
+    "num_transitions": 11099,
+    "num_trajectories": 50
+  },
+  "lift_pot_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.08297546207904816,
+        1.409603238105774,
+        0.9623388648033142,
+        -0.41438642144203186,
+        0.6548429131507874,
+        -0.1665104776620865,
+        0.3988376259803772,
+        0.07074239104986191,
+        1.438857078552246,
+        1.0271384716033936,
+        -0.4811238646507263,
+        -0.6411229968070984,
+        0.13417655229568481,
+        0.3988376259803772
+      ],
+      "std": [
+        0.07541920989751816,
+        0.9829710721969604,
+        0.7201086282730103,
+        0.4319354295730591,
+        0.44529980421066284,
+        0.2749307453632355,
+        0.25010964274406433,
+        0.07464443892240524,
+        1.0042012929916382,
+        0.7712593078613281,
+        0.46114370226860046,
+        0.4362685978412628,
+        0.2527497708797455,
+        0.25010964274406433
+      ],
+      "max": [
+        0.044971514493227005,
+        2.7116494178771973,
+        2.7667176723480225,
+        0.16915999352931976,
+        1.0692775249481201,
+        0.3872540295124054,
+        1.0,
+        0.3003857135772705,
+        2.6776700019836426,
+        2.6789329051971436,
+        0.1484600007534027,
+        0.0,
+        0.8275586366653442,
+        1.0
+      ],
+      "min": [
+        -0.2611783444881439,
+        0.0,
+        0.0,
+        -1.7497934103012085,
+        0.0,
+        -0.7937148809432983,
+        0.0,
+        -0.036408890038728714,
+        0.0,
+        0.0,
+        -1.7768168449401855,
+        -1.0369138717651367,
+        -0.34945493936538696,
+        0.0
+      ],
+      "q01": [
+        -0.24835357069969177,
+        0.0,
+        0.0,
+        -1.4652207350730897,
+        0.0,
+        -0.7823562324047089,
+        0.0,
+        -0.02311648927628994,
+        0.0,
+        0.0,
+        -1.5518183708190918,
+        -1.0312373971939086,
+        -0.27689045667648315,
+        0.0
+      ],
+      "q99": [
+        0.018272288143634796,
+        2.6518030166625977,
+        2.3128650331497194,
+        0.1131945550441742,
+        1.0648717546463013,
+        0.30490371704101565,
+        0.9623115658760071,
+        0.2519652831554413,
+        2.6547678661346437,
+        2.426756353378296,
+        0.004153958521783352,
+        0.0,
+        0.7562372756004334,
+        0.9623115658760071
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.08176180720329285,
+        1.390768051147461,
+        0.9472193121910095,
+        -0.4064285159111023,
+        0.6457239389419556,
+        -0.1636435091495514,
+        0.4078385531902313,
+        0.06947626918554306,
+        1.4194576740264893,
+        1.0109381675720215,
+        -0.47207584977149963,
+        -0.6321477890014648,
+        0.1318783462047577,
+        0.4078385531902313
+      ],
+      "std": [
+        0.07548711448907852,
+        0.9894453287124634,
+        0.7214877009391785,
+        0.4296827018260956,
+        0.44820061326026917,
+        0.2734803557395935,
+        0.253564715385437,
+        0.074403315782547,
+        1.0106186866760254,
+        0.7725314497947693,
+        0.4590045213699341,
+        0.4390813708305359,
+        0.2512795925140381,
+        0.253564715385437
+      ],
+      "max": [
+        0.044971514493227005,
+        2.7116494178771973,
+        2.7667176723480225,
+        0.16915999352931976,
+        1.0692775249481201,
+        0.3872540295124054,
+        1.0,
+        0.30037084221839905,
+        2.6776700019836426,
+        2.6789329051971436,
+        0.1484600007534027,
+        0.0,
+        0.8275586366653442,
+        1.0
+      ],
+      "min": [
+        -0.2611783444881439,
+        0.0,
+        0.0,
+        -1.7497934103012085,
+        0.0,
+        -0.7937148809432983,
+        0.0,
+        -0.036408890038728714,
+        0.0,
+        0.0,
+        -1.7768168449401855,
+        -1.0369138717651367,
+        -0.34945493936538696,
+        0.0
+      ],
+      "q01": [
+        -0.24835357069969177,
+        0.0,
+        0.0,
+        -1.4640827035903932,
+        0.0,
+        -0.7821481037139892,
+        0.0,
+        -0.02311648927628994,
+        0.0,
+        0.0,
+        -1.5469084978103638,
+        -1.0312373971939086,
+        -0.27689045667648315,
+        0.0
+      ],
+      "q99": [
+        0.018272288143634796,
+        2.6518030166625977,
+        2.2994370603561403,
+        0.1131945550441742,
+        1.0648064422607422,
+        0.30490371704101565,
+        1.0,
+        0.2516305303573608,
+        2.654694414138794,
+        2.3750886774063114,
+        0.004153958521783352,
+        0.0,
+        0.756147563457489,
+        1.0
+      ]
+    },
+    "num_transitions": 5555,
+    "num_trajectories": 50
+  },
+  "move_can_pot_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.03227013349533081,
+        0.9706242084503174,
+        0.8449752926826477,
+        -0.4254174828529358,
+        0.05570398271083832,
+        0.09763211756944656,
+        0.7630919218063354,
+        0.043113451451063156,
+        1.036581039428711,
+        0.9449251294136047,
+        -0.5384683012962341,
+        -0.01411368977278471,
+        -0.09070954471826553,
+        0.7476586103439331
+      ],
+      "std": [
+        0.11224070936441422,
+        1.1117055416107178,
+        1.0112414360046387,
+        0.6318230628967285,
+        0.18493546545505524,
+        0.2674776613712311,
+        0.40844425559043884,
+        0.10649054497480392,
+        1.1204936504364014,
+        1.0428167581558228,
+        0.589298665523529,
+        0.058494143187999725,
+        0.27779391407966614,
+        0.41657936573028564
+      ],
+      "max": [
+        0.3149413466453552,
+        3.0223748683929443,
+        3.342350721359253,
+        1.4929821491241455,
+        1.153774380683899,
+        0.9257752299308777,
+        1.0,
+        0.32490795850753784,
+        2.995034694671631,
+        3.3333518505096436,
+        0.0,
+        0.11092565208673477,
+        0.318450003862381,
+        1.0
+      ],
+      "min": [
+        -0.3494536280632019,
+        0.0,
+        0.0,
+        -1.7953904867172241,
+        -0.13677525520324707,
+        -0.3176078498363495,
+        0.0,
+        -0.0804772898554802,
+        0.0,
+        0.0,
+        -1.8121548891067505,
+        -0.21740999817848206,
+        -0.8863733410835266,
+        0.0
+      ],
+      "q01": [
+        -0.3341374397277832,
+        0.0,
+        0.0,
+        -1.6472991704940796,
+        -0.11959030479192734,
+        -0.30239319801330566,
+        0.0,
+        -0.0527563564479351,
+        0.0,
+        0.0,
+        -1.6625858545303345,
+        -0.19942482709884643,
+        -0.830798065662384,
+        0.0
+      ],
+      "q99": [
+        0.2560995936393735,
+        2.8609605312347384,
+        3.1173167228698704,
+        1.3075437545776367,
+        1.0580827236175536,
+        0.9104878306388855,
+        1.0,
+        0.31480422616004944,
+        2.913422060012817,
+        3.2138250827789308,
+        0.0,
+        0.09690605103969574,
+        0.31249096989631653,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.03136930614709854,
+        0.9640669822692871,
+        0.8403865694999695,
+        -0.423330694437027,
+        0.0556751973927021,
+        0.09843266755342484,
+        0.7630919218063354,
+        0.042088694870471954,
+        1.0294307470321655,
+        0.939507782459259,
+        -0.5354413986206055,
+        -0.014315903186798096,
+        -0.09174654632806778,
+        0.7476586103439331
+      ],
+      "std": [
+        0.11126016825437546,
+        1.1112853288650513,
+        1.0116392374038696,
+        0.6310778856277466,
+        0.1844586580991745,
+        0.2667086720466614,
+        0.40844425559043884,
+        0.10545298457145691,
+        1.1204065084457397,
+        1.0435622930526733,
+        0.5897011756896973,
+        0.05830603092908859,
+        0.2768862843513489,
+        0.41657936573028564
+      ],
+      "max": [
+        0.3149413466453552,
+        3.0223748683929443,
+        3.342350721359253,
+        1.4929821491241455,
+        1.153774380683899,
+        0.9257752299308777,
+        1.0,
+        0.32490795850753784,
+        2.995034694671631,
+        3.3333518505096436,
+        0.0,
+        0.11092565208673477,
+        0.318450003862381,
+        1.0
+      ],
+      "min": [
+        -0.3494536280632019,
+        0.0,
+        0.0,
+        -1.7953904867172241,
+        -0.13677525520324707,
+        -0.3176078498363495,
+        0.0,
+        -0.0804772898554802,
+        0.0,
+        0.0,
+        -1.8121548891067505,
+        -0.21740999817848206,
+        -0.8863733410835266,
+        0.0
+      ],
+      "q01": [
+        -0.3341374397277832,
+        0.0,
+        0.0,
+        -1.6472991704940796,
+        -0.11959030479192734,
+        -0.30239319801330566,
+        0.0,
+        -0.0527563564479351,
+        0.0,
+        0.0,
+        -1.6625858545303345,
+        -0.19942482709884643,
+        -0.830798065662384,
+        0.0
+      ],
+      "q99": [
+        0.2560995936393735,
+        2.8609605312347384,
+        3.1173167228698704,
+        1.3057452917098997,
+        1.0580827236175536,
+        0.9104878306388855,
+        1.0,
+        0.31480422616004944,
+        2.913422060012817,
+        3.2138250827789308,
+        0.0,
+        0.09655882418155662,
+        0.3124178946018219,
+        1.0
+      ]
+    },
+    "num_transitions": 7581,
+    "num_trajectories": 50
+  },
+  "open_laptop_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.412982702255249,
+        0.9725632071495056,
+        0.5560179352760315,
+        -0.018646035343408585,
+        -0.1403660625219345,
+        0.671284019947052,
+        0.5757868885993958,
+        0.3011881411075592,
+        0.795698344707489,
+        0.4804641604423523,
+        -0.04225534945726395,
+        0.059194255620241165,
+        0.7393441200256348,
+        0.648875892162323
+      ],
+      "std": [
+        0.41707736253738403,
+        0.9429622888565063,
+        0.6425602436065674,
+        0.4222852289676666,
+        0.2817792296409607,
+        0.6755929589271545,
+        0.48893213272094727,
+        0.3684118092060089,
+        0.9421394467353821,
+        0.6803411245346069,
+        0.4267505407333374,
+        0.22786928713321686,
+        0.8749793171882629,
+        0.47337010502815247
+      ],
+      "max": [
+        0.014050900936126709,
+        2.7143473625183105,
+        2.9012439250946045,
+        1.1832900047302246,
+        0.8149839043617249,
+        2.0226521492004395,
+        1.0,
+        1.3892728090286255,
+        2.72947359085083,
+        2.906212091445923,
+        1.3124799728393555,
+        1.0027532577514648,
+        2.6277825832366943,
+        1.0
+      ],
+      "min": [
+        -1.3947231769561768,
+        0.0,
+        0.0,
+        -1.5809979438781738,
+        -1.1472011804580688,
+        0.0,
+        0.0,
+        -0.005201177205890417,
+        0.0,
+        -0.009437700733542442,
+        -1.6318897008895874,
+        -0.7902398705482483,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -1.213874478340149,
+        0.0,
+        0.0,
+        -1.27146577835083,
+        -0.886477215886116,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3470568656921387,
+        -0.5466296076774597,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.417337417602539,
+        2.35109806060791,
+        0.9501821488142007,
+        0.5932847791910171,
+        1.9652117490768433,
+        1.0,
+        1.1480320692062378,
+        2.519510269165039,
+        2.53169584274292,
+        1.0674813234806055,
+        0.8172210627794251,
+        2.301086187362671,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.41133424639701843,
+        0.9674974679946899,
+        0.5518006682395935,
+        -0.016732074320316315,
+        -0.1401025503873825,
+        0.6682498455047607,
+        0.5781423449516296,
+        0.30009010434150696,
+        0.7918621301651001,
+        0.4770472049713135,
+        -0.040619634091854095,
+        0.059146538376808167,
+        0.7364427447319031,
+        0.6505815386772156
+      ],
+      "std": [
+        0.41728851199150085,
+        0.9423418641090393,
+        0.6399604678153992,
+        0.4200330376625061,
+        0.2817077338695526,
+        0.6755438446998596,
+        0.48856765031814575,
+        0.36834537982940674,
+        0.9407066106796265,
+        0.6772210597991943,
+        0.424578994512558,
+        0.22778856754302979,
+        0.8745430111885071,
+        0.47281336784362793
+      ],
+      "max": [
+        0.014050900936126709,
+        2.7143473625183105,
+        2.9012439250946045,
+        1.1832900047302246,
+        0.8149839043617249,
+        2.0226521492004395,
+        1.0,
+        1.3892728090286255,
+        2.72947359085083,
+        2.906212091445923,
+        1.3124799728393555,
+        1.0027532577514648,
+        2.6277825832366943,
+        1.0
+      ],
+      "min": [
+        -1.3947231769561768,
+        0.0,
+        0.0,
+        -1.5809979438781738,
+        -1.1472011804580688,
+        0.0,
+        0.0,
+        -0.005201177205890417,
+        0.0,
+        -0.009437700733542442,
+        -1.6318897008895874,
+        -0.7902398705482483,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -1.213874478340149,
+        0.0,
+        0.0,
+        -1.27146577835083,
+        -0.886477215886116,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3470568656921387,
+        -0.5466296076774597,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.417337417602539,
+        2.35109806060791,
+        0.9501821488142007,
+        0.5932847791910171,
+        1.9652117490768433,
+        1.0,
+        1.1480320692062378,
+        2.519510269165039,
+        2.53169584274292,
+        1.0674813234806055,
+        0.8172210627794251,
+        2.301086187362671,
+        1.0
+      ]
+    },
+    "num_transitions": 12312,
+    "num_trajectories": 50
+  },
+  "pick_dual_bottles_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.15950368344783783,
+        1.7896647453308105,
+        0.9439552426338196,
+        0.7748593091964722,
+        0.5115674138069153,
+        0.05964143946766853,
+        0.49804794788360596,
+        0.1409446895122528,
+        1.8259799480438232,
+        1.0187904834747314,
+        0.7640450596809387,
+        -0.5400498509407043,
+        -0.017349114641547203,
+        0.49804794788360596
+      ],
+      "std": [
+        0.20802833139896393,
+        0.7114025950431824,
+        0.49483397603034973,
+        0.31811821460723877,
+        0.31533852219581604,
+        0.07191983610391617,
+        0.48165053129196167,
+        0.2233102172613144,
+        0.717391312122345,
+        0.5089762806892395,
+        0.29336756467819214,
+        0.3482920825481415,
+        0.050167474895715714,
+        0.48165053129196167
+      ],
+      "max": [
+        0.39062753319740295,
+        2.8961520195007324,
+        2.413680076599121,
+        1.6144700050354004,
+        1.4948179721832275,
+        0.6830681562423706,
+        1.0,
+        0.6129000186920166,
+        2.735783815383911,
+        2.0820229053497314,
+        1.28603994846344,
+        0.0,
+        0.05678669363260269,
+        1.0
+      ],
+      "min": [
+        -0.5722799897193909,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.04302142560482025,
+        0.0,
+        -0.3316712975502014,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.37959623336792,
+        -0.302682101726532,
+        0.0
+      ],
+      "q01": [
+        -0.5586331933736801,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.010444268118590117,
+        0.0,
+        -0.26194658875465393,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.2651588320732117,
+        -0.17471149563789368,
+        0.0
+      ],
+      "q99": [
+        0.2784392535686493,
+        2.7243048548698425,
+        1.9894878566265106,
+        1.431083768606186,
+        1.2972519397735596,
+        0.3371240422129631,
+        1.0,
+        0.5960070490837097,
+        2.733114242553711,
+        2.0299019813537598,
+        1.236779510974884,
+        0.0,
+        0.04197721276432276,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.15498977899551392,
+        1.7792388200759888,
+        0.9396310448646545,
+        0.769199013710022,
+        0.5095281600952148,
+        0.0594169907271862,
+        0.5062099099159241,
+        0.13609662652015686,
+        1.814998984336853,
+        1.0140470266342163,
+        0.7581976652145386,
+        -0.538648784160614,
+        -0.01742432452738285,
+        0.5062099099159241
+      ],
+      "std": [
+        0.20542342960834503,
+        0.7279935479164124,
+        0.5007017254829407,
+        0.32545188069343567,
+        0.3178229331970215,
+        0.07205121219158173,
+        0.4816075265407562,
+        0.21984270215034485,
+        0.7347503900527954,
+        0.5156906247138977,
+        0.3011578619480133,
+        0.35011228919029236,
+        0.050126101821660995,
+        0.4816075265407562
+      ],
+      "max": [
+        0.39062753319740295,
+        2.8961520195007324,
+        2.413680076599121,
+        1.6144700050354004,
+        1.4948179721832275,
+        0.6830681562423706,
+        1.0,
+        0.6129000186920166,
+        2.735783815383911,
+        2.0820229053497314,
+        1.28603994846344,
+        0.0,
+        0.05678669363260269,
+        1.0
+      ],
+      "min": [
+        -0.5722799897193909,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -0.04302142560482025,
+        0.0,
+        -0.3316712975502014,
+        0.0,
+        0.0,
+        0.0,
+        -1.37959623336792,
+        -0.302682101726532,
+        0.0
+      ],
+      "q01": [
+        -0.5585764646530151,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.010444268118590117,
+        0.0,
+        -0.26194658875465393,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.2651588320732117,
+        -0.17471149563789368,
+        0.0
+      ],
+      "q99": [
+        0.2784392535686493,
+        2.7243048548698425,
+        1.9894878566265106,
+        1.431083768606186,
+        1.2972519397735596,
+        0.3371240422129631,
+        1.0,
+        0.5959392786026001,
+        2.733114242553711,
+        2.0299019813537598,
+        1.236779510974884,
+        0.0,
+        0.04197721276432276,
+        1.0
+      ]
+    },
+    "num_transitions": 6126,
+    "num_trajectories": 50
+  },
+  "place_dual_shoes_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.364918053150177,
+        1.520833969116211,
+        1.0176814794540405,
+        -0.6050214767456055,
+        0.016822682693600655,
+        -0.1008746474981308,
+        0.6267212629318237,
+        0.1734830141067505,
+        1.9947360754013062,
+        1.4386961460113525,
+        -0.9159395098686218,
+        -0.003797657787799835,
+        -0.27571171522140503,
+        0.31392714381217957
+      ],
+      "std": [
+        1.027938961982727,
+        0.9228556752204895,
+        0.6396123766899109,
+        0.3982831537723541,
+        0.05350078269839287,
+        1.1809768676757812,
+        0.4633759558200836,
+        0.302899032831192,
+        0.5275591015815735,
+        0.43367037177085876,
+        0.289938747882843,
+        0.02490071952342987,
+        1.0572752952575684,
+        0.44288790225982666
+      ],
+      "max": [
+        5.369949817657471,
+        2.5988383293151855,
+        2.2322678565979004,
+        2.1778516384074464e-05,
+        0.27490007877349854,
+        3.83882999420166,
+        1.0,
+        0.7725619673728943,
+        2.6282360553741455,
+        2.372544288635254,
+        0.0,
+        0.07164999842643738,
+        2.342289924621582,
+        1.0
+      ],
+      "min": [
+        -7.180570602416992,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4090766906738281,
+        -0.10993228107690811,
+        -2.4864256381988525,
+        0.0,
+        -0.2831000089645386,
+        0.0,
+        0.0,
+        -1.5554989576339722,
+        -0.13825319707393646,
+        -4.031702518463135,
+        0.0
+      ],
+      "q01": [
+        -6.867993459701538,
+        2.9715695291088196e-06,
+        -3.064708653255366e-05,
+        -1.2694409608840942,
+        -0.06486005619168282,
+        -2.466008424758911,
+        0.0,
+        -0.26483604311943054,
+        0.009487488549202682,
+        0.009014308238402009,
+        -1.411148397922516,
+        -0.11009304970502853,
+        -3.9720680713653564,
+        0.0
+      ],
+      "q99": [
+        1.2002487826347954,
+        2.494779109954834,
+        1.9488718807697298,
+        2.1778516384074464e-05,
+        0.2206193839013577,
+        3.605363287925731,
+        1.0,
+        0.7506802678108215,
+        2.5776290893554688,
+        2.192576406002045,
+        -0.005188394398428484,
+        0.057537742741406016,
+        2.3226377964019775,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.3650052845478058,
+        1.520833969116211,
+        1.0176814794540405,
+        -0.6050214767456055,
+        0.016822639852762222,
+        -0.1008746474981308,
+        0.6267212629318237,
+        0.17031559348106384,
+        1.9842966794967651,
+        1.4307619333267212,
+        -0.9114848971366882,
+        -0.0037371169310063124,
+        -0.2758965492248535,
+        0.31392714381217957
+      ],
+      "std": [
+        1.0279076099395752,
+        0.9228557348251343,
+        0.6396121978759766,
+        0.3982830345630646,
+        0.05350079387426376,
+        1.1809768676757812,
+        0.4633759558200836,
+        0.30088669061660767,
+        0.5429739952087402,
+        0.4430801570415497,
+        0.29594969749450684,
+        0.02473057620227337,
+        1.0498296022415161,
+        0.44288793206214905
+      ],
+      "max": [
+        5.369949817657471,
+        2.5988383293151855,
+        2.2322678565979004,
+        2.1778516384074464e-05,
+        0.27490007877349854,
+        3.83882999420166,
+        1.0,
+        0.7725619673728943,
+        2.6282360553741455,
+        2.372544288635254,
+        0.0,
+        0.07164999842643738,
+        2.342289924621582,
+        1.0
+      ],
+      "min": [
+        -7.180570602416992,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4090766906738281,
+        -0.10993228107690811,
+        -2.4864256381988525,
+        0.0,
+        -0.2831000089645386,
+        0.0,
+        0.0,
+        -1.5554989576339722,
+        -0.13825319707393646,
+        -4.031702518463135,
+        0.0
+      ],
+      "q01": [
+        -6.867993459701538,
+        0.0,
+        -3.064708653255366e-05,
+        -1.2694409608840942,
+        -0.06486005619168282,
+        -2.466008424758911,
+        0.0,
+        -0.26483604311943054,
+        0.0,
+        0.0,
+        -1.411148397922516,
+        -0.11009304970502853,
+        -3.9709057807922363,
+        0.0
+      ],
+      "q99": [
+        1.2002487826347954,
+        2.494779109954834,
+        1.9488718807697298,
+        2.1778516384074464e-05,
+        0.2206193839013577,
+        3.605363287925731,
+        1.0,
+        0.7506802678108215,
+        2.5776290893554688,
+        2.192576406002045,
+        0.0,
+        0.056274176910520335,
+        2.3226377964019775,
+        1.0
+      ]
+    },
+    "num_transitions": 11474,
+    "num_trajectories": 50
+  },
+  "place_object_basket_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.17315083742141724,
+        1.1054847240447998,
+        0.9055297374725342,
+        -0.3234107792377472,
+        0.21469952166080475,
+        0.17225050926208496,
+        0.7572181820869446,
+        0.16253036260604858,
+        0.9573038816452026,
+        0.8219836950302124,
+        -0.3597754240036011,
+        -0.10522812604904175,
+        -0.17085255682468414,
+        0.7928103804588318
+      ],
+      "std": [
+        0.21726401150226593,
+        1.0674771070480347,
+        0.9510499238967896,
+        0.7475572824478149,
+        0.4299476444721222,
+        0.44102591276168823,
+        0.4120478928089142,
+        0.3323862552642822,
+        1.0525022745132446,
+        0.9476361870765686,
+        0.7507379055023193,
+        0.3281928598880768,
+        0.524770200252533,
+        0.38940587639808655
+      ],
+      "max": [
+        0.02667570859193802,
+        3.778841733932495,
+        4.568304061889648,
+        1.0729998350143433,
+        1.2834042310714722,
+        1.6689540147781372,
+        1.0,
+        0.8685938715934753,
+        2.6522276401519775,
+        3.264349937438965,
+        1.1015950441360474,
+        1.1631274223327637,
+        3.160046100616455,
+        1.0
+      ],
+      "min": [
+        -0.858056902885437,
+        0.0,
+        -3.064708653255366e-05,
+        -1.8265033960342407,
+        -0.028474153950810432,
+        -0.8472005128860474,
+        0.0,
+        -2.803896188735962,
+        -0.4660651385784149,
+        -2.5819708753260784e-05,
+        -1.9893873929977417,
+        -1.1286083459854126,
+        -1.736908197402954,
+        0.0
+      ],
+      "q01": [
+        -0.6967605352401733,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7222540175914764,
+        -0.014083670675754547,
+        -0.5925237935781479,
+        0.0,
+        -0.04488968480378389,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7749713563919067,
+        -1.1208465099334717,
+        -1.7055805158615112,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.777959144115448,
+        2.902973175048828,
+        1.0099876379966732,
+        1.272336483001709,
+        1.5119220542907714,
+        1.0,
+        0.8364987969398499,
+        2.5737154483795166,
+        2.664341926574707,
+        1.0096371030807485,
+        0.11334303751587624,
+        0.8461964726448059,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.17225174605846405,
+        1.101753830909729,
+        0.9016473889350891,
+        -0.3206050992012024,
+        0.21468140184879303,
+        0.17057755589485168,
+        0.7589125037193298,
+        0.1611548811197281,
+        0.9520825743675232,
+        0.8164799809455872,
+        -0.35582447052001953,
+        -0.10522229224443436,
+        -0.16855837404727936,
+        0.7951502203941345
+      ],
+      "std": [
+        0.21675990521907806,
+        1.0675125122070312,
+        0.9500465393066406,
+        0.7456965446472168,
+        0.4299568831920624,
+        0.43978309631347656,
+        0.4109635651111603,
+        0.3318731486797333,
+        1.0517019033432007,
+        0.9455519914627075,
+        0.7481663227081299,
+        0.32819226384162903,
+        0.5233535766601562,
+        0.3876449465751648
+      ],
+      "max": [
+        0.02667570859193802,
+        3.778841733932495,
+        4.568304061889648,
+        1.0729998350143433,
+        1.2834042310714722,
+        1.6689540147781372,
+        1.0,
+        0.8685938715934753,
+        2.6522276401519775,
+        3.264349937438965,
+        1.1015950441360474,
+        1.1631274223327637,
+        3.160046100616455,
+        1.0
+      ],
+      "min": [
+        -0.858056902885437,
+        0.0,
+        -3.064708653255366e-05,
+        -1.8265033960342407,
+        -0.028474153950810432,
+        -0.8472005128860474,
+        0.0,
+        -2.803896188735962,
+        -0.4660651385784149,
+        -2.5819708753260784e-05,
+        -1.9893873929977417,
+        -1.1286083459854126,
+        -1.736908197402954,
+        0.0
+      ],
+      "q01": [
+        -0.6967605352401733,
+        0.0,
+        -3.064708653255366e-05,
+        -1.720620036125183,
+        -0.014083670675754547,
+        -0.5925237935781479,
+        0.0,
+        -0.04488968480378389,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7743152236938478,
+        -1.1208465099334717,
+        -1.7055805158615112,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.777959144115448,
+        2.902973175048828,
+        1.0099876379966732,
+        1.272336483001709,
+        1.5119220542907714,
+        1.0,
+        0.8364987969398499,
+        2.5737154483795166,
+        2.664341926574707,
+        1.0096371030807485,
+        0.11334303751587624,
+        0.8461964726448059,
+        1.0
+      ]
+    },
+    "num_transitions": 12394,
+    "num_trajectories": 50
+  },
+  "place_phone_stand_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.24955666065216064,
+        0.96904057264328,
+        0.6381116509437561,
+        -0.24100831151008606,
+        -0.11241041123867035,
+        -0.17180098593235016,
+        0.7342271208763123,
+        0.18775422871112823,
+        0.7277160286903381,
+        0.4949023425579071,
+        -0.1834067404270172,
+        0.0859995111823082,
+        0.13992515206336975,
+        0.8067823052406311
+      ],
+      "std": [
+        0.27407583594322205,
+        0.9312117695808411,
+        0.6364781856536865,
+        0.3659789264202118,
+        0.20980769395828247,
+        0.30180302262306213,
+        0.41827327013015747,
+        0.25742045044898987,
+        0.9351555705070496,
+        0.653581440448761,
+        0.33380553126335144,
+        0.19428309798240662,
+        0.28798776865005493,
+        0.3758675456047058
+      ],
+      "max": [
+        0.0,
+        2.447045087814331,
+        2.13200044631958,
+        0.27845299243927,
+        0.08664697408676147,
+        0.3559088408946991,
+        1.0,
+        0.881425678730011,
+        2.4448471069335938,
+        2.0905659198760986,
+        0.17398181557655334,
+        0.8084141612052917,
+        1.3334383964538574,
+        1.0
+      ],
+      "min": [
+        -0.9803099632263184,
+        0.0,
+        0.0,
+        -1.340118169784546,
+        -0.8613136410713196,
+        -1.3788976669311523,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2530916929244995,
+        -0.021880000829696655,
+        -0.4965520203113556,
+        0.0
+      ],
+      "q01": [
+        -0.8396719813346862,
+        0.0,
+        0.0,
+        -1.0591385531425477,
+        -0.7201460123062133,
+        -1.0765761065483093,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0728944265842437,
+        -0.015176629358902573,
+        -0.27935048937797546,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.360727548599243,
+        1.9245160818099976,
+        0.2115602046251297,
+        0.005798054859042168,
+        0.3227096199989319,
+        1.0,
+        0.7152422976493827,
+        2.348403215408325,
+        1.9352505207061768,
+        0.14278633892536163,
+        0.6668316721916199,
+        1.122056484222412,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.2470388114452362,
+        0.959843635559082,
+        0.6315163373947144,
+        -0.2413448542356491,
+        -0.11027531325817108,
+        -0.17068681120872498,
+        0.7342271208763123,
+        0.18596167862415314,
+        0.7207176685333252,
+        0.48970282077789307,
+        -0.18350425362586975,
+        0.08433938771486282,
+        0.13906404376029968,
+        0.8067823052406311
+      ],
+      "std": [
+        0.2737008035182953,
+        0.9307196736335754,
+        0.635271430015564,
+        0.365650475025177,
+        0.20842938125133514,
+        0.30195745825767517,
+        0.41827327013015747,
+        0.2567901611328125,
+        0.9326195120811462,
+        0.6510723233222961,
+        0.33370161056518555,
+        0.19280125200748444,
+        0.2880008816719055,
+        0.3758675456047058
+      ],
+      "max": [
+        0.0,
+        2.447045087814331,
+        2.13200044631958,
+        0.27845299243927,
+        0.08664697408676147,
+        0.3559088408946991,
+        1.0,
+        0.881425678730011,
+        2.4448471069335938,
+        2.0905659198760986,
+        0.17398181557655334,
+        0.8084141612052917,
+        1.3334383964538574,
+        1.0
+      ],
+      "min": [
+        -0.9803099632263184,
+        0.0,
+        0.0,
+        -1.340118169784546,
+        -0.8613136410713196,
+        -1.3788976669311523,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2530916929244995,
+        -0.021880000829696655,
+        -0.4965520203113556,
+        0.0
+      ],
+      "q01": [
+        -0.8396719813346862,
+        0.0,
+        0.0,
+        -1.0591385531425477,
+        -0.7201460123062133,
+        -1.0765761065483093,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0728944265842437,
+        -0.015176629358902573,
+        -0.27935048937797546,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.360727548599243,
+        1.9245160818099976,
+        0.2115602046251297,
+        0.005798054859042168,
+        0.3227096199989319,
+        1.0,
+        0.7152422976493827,
+        2.348403215408325,
+        1.929482665061947,
+        0.14278633892536163,
+        0.6668316721916199,
+        1.122056484222412,
+        1.0
+      ]
+    },
+    "num_transitions": 6340,
+    "num_trajectories": 50
+  },
+  "put_bottles_dustbin_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.10476312041282654,
+        1.279449462890625,
+        0.5679159164428711,
+        0.5262239575386047,
+        0.22428759932518005,
+        0.043430764228105545,
+        0.6643036603927612,
+        0.19642633199691772,
+        1.117813229560852,
+        0.7129987478256226,
+        0.34945419430732727,
+        -0.2572241723537445,
+        -0.014616748318076134,
+        0.6229138970375061
+      ],
+      "std": [
+        0.25748223066329956,
+        0.9038821458816528,
+        0.44731849431991577,
+        0.5311910510063171,
+        0.24035809934139252,
+        0.05469735711812973,
+        0.44946926832199097,
+        0.2710811495780945,
+        0.9252318739891052,
+        0.6049585342407227,
+        0.31488096714019775,
+        0.2908095717430115,
+        0.04302773252129555,
+        0.4715794026851654
+      ],
+      "max": [
+        0.37749066948890686,
+        2.792454242706299,
+        1.926819086074829,
+        1.7764710187911987,
+        1.1897499561309814,
+        0.26964041590690613,
+        1.0,
+        0.5676500201225281,
+        2.6290316581726074,
+        2.1055216789245605,
+        1.4273184537887573,
+        2.08674136956688e-05,
+        0.08746904134750366,
+        1.0
+      ],
+      "min": [
+        -0.5788599848747253,
+        -0.000386573257856071,
+        -0.005483707413077354,
+        -0.016073711216449738,
+        -0.0011532115750014782,
+        -0.05382127687335014,
+        0.0,
+        -0.5238198041915894,
+        -1.2773498383467086e-05,
+        -3.102514165220782e-05,
+        -1.7486241631559096e-05,
+        -1.2793614864349365,
+        -0.28817570209503174,
+        0.0
+      ],
+      "q01": [
+        -0.5524951219558716,
+        -4.5852862058382016e-06,
+        -3.064708653255366e-05,
+        -1.1295316653558984e-05,
+        -6.482168373622699e-06,
+        -0.017715569883584976,
+        0.0,
+        -0.3273748874664307,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7486239812569693e-05,
+        -1.061243543624878,
+        -0.16889580547809602,
+        0.0
+      ],
+      "q99": [
+        0.18636572360992432,
+        2.6412384510040283,
+        1.7621351480484009,
+        1.5735922956466664,
+        0.9350311040878294,
+        0.17157290935516348,
+        1.0,
+        0.5604766869544983,
+        2.5446243667602535,
+        1.8965028524398804,
+        1.0255632400512695,
+        0.0,
+        0.07489971071481705,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.10503867268562317,
+        1.2768235206604004,
+        0.566667914390564,
+        0.5260285139083862,
+        0.2240736186504364,
+        0.043224893510341644,
+        0.6643036603927612,
+        0.19641055166721344,
+        1.117813229560852,
+        0.7129987478256226,
+        0.34945419430732727,
+        -0.2572241723537445,
+        -0.014616748318076134,
+        0.6229138970375061
+      ],
+      "std": [
+        0.257255882024765,
+        0.9052372574806213,
+        0.44780004024505615,
+        0.5313783884048462,
+        0.2404986470937729,
+        0.05462057143449783,
+        0.4494692385196686,
+        0.27109846472740173,
+        0.9252316355705261,
+        0.6049585342407227,
+        0.31488096714019775,
+        0.2908095717430115,
+        0.04302773252129555,
+        0.4715794026851654
+      ],
+      "max": [
+        0.37749066948890686,
+        2.792454242706299,
+        1.926819086074829,
+        1.7764710187911987,
+        1.1897499561309814,
+        0.26964041590690613,
+        1.0,
+        0.5676500201225281,
+        2.6290316581726074,
+        2.1055216789245605,
+        1.4273184537887573,
+        2.08674136956688e-05,
+        0.08746904134750366,
+        1.0
+      ],
+      "min": [
+        -0.5788599848747253,
+        -0.000386573257856071,
+        -0.005483707413077354,
+        -0.016073711216449738,
+        -0.0011532115750014782,
+        -0.05382127687335014,
+        0.0,
+        -0.5238198041915894,
+        -1.2773498383467086e-05,
+        -3.102514165220782e-05,
+        -1.7486241631559096e-05,
+        -1.2793614864349365,
+        -0.28817570209503174,
+        0.0
+      ],
+      "q01": [
+        -0.5524951219558716,
+        -4.5852862058382016e-06,
+        -3.064708653255366e-05,
+        -1.1295316653558984e-05,
+        -6.482168373622699e-06,
+        -0.017715569883584976,
+        0.0,
+        -0.3273748874664307,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7486239812569693e-05,
+        -1.061243543624878,
+        -0.16889580547809602,
+        0.0
+      ],
+      "q99": [
+        0.18636572360992432,
+        2.6412384510040283,
+        1.7621351480484009,
+        1.5735922956466664,
+        0.9350311040878294,
+        0.17157290935516348,
+        1.0,
+        0.5604766869544983,
+        2.5446243667602535,
+        1.8965028524398804,
+        1.0255632400512695,
+        0.0,
+        0.07489971071481705,
+        1.0
+      ]
+    },
+    "num_transitions": 31457,
+    "num_trajectories": 50
+  },
+  "put_object_cabinet_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.28438690304756165,
+        1.764165997505188,
+        1.1697032451629639,
+        -0.8346169590950012,
+        -0.027913035824894905,
+        0.2120014876127243,
+        0.2626643180847168,
+        0.7117581367492676,
+        1.5304840803146362,
+        0.6800471544265747,
+        0.7674376368522644,
+        0.6864986419677734,
+        -0.9722089767456055,
+        0.4657360017299652
+      ],
+      "std": [
+        0.24526149034500122,
+        0.512263298034668,
+        0.6820644736289978,
+        0.5534589886665344,
+        0.1659320443868637,
+        0.859703004360199,
+        0.421505331993103,
+        0.5065756440162659,
+        0.9997316002845764,
+        0.4915132224559784,
+        0.6616247296333313,
+        0.5067867636680603,
+        0.763378381729126,
+        0.490205854177475
+      ],
+      "max": [
+        0.08587173372507095,
+        2.7109200954437256,
+        3.1437699794769287,
+        1.352994680404663,
+        0.03168417513370514,
+        1.9688830375671387,
+        1.0,
+        1.2608709335327148,
+        2.8519444465637207,
+        3.266448497772217,
+        1.3512014150619507,
+        1.2738021612167358,
+        0.7937034964561462,
+        1.0
+      ],
+      "min": [
+        -1.1788203716278076,
+        0.0,
+        0.0,
+        -1.865718960762024,
+        -1.1659986972808838,
+        -1.1686975955963135,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.9566882848739624,
+        -0.005439999978989363,
+        -1.559630036354065,
+        0.0
+      ],
+      "q01": [
+        -1.1621215343475342,
+        0.0,
+        0.0,
+        -1.8261551237106324,
+        -1.1501946449279785,
+        -1.1634292602539062,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3072348833084106,
+        -0.004720241297036409,
+        -1.554312481880188,
+        0.0
+      ],
+      "q99": [
+        0.07353582233190536,
+        2.61279296875,
+        2.9014140892028806,
+        1.318111538887024,
+        0.02163825549185276,
+        1.9688830375671387,
+        1.0,
+        1.2599315643310547,
+        2.408996105194092,
+        1.3551753759384155,
+        1.3495060205459595,
+        1.2695887088775635,
+        0.7937034964561462,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.2818736433982849,
+        1.7552909851074219,
+        1.161139726638794,
+        -0.8293845057487488,
+        -0.02772539108991623,
+        0.21406246721744537,
+        0.26281240582466125,
+        0.707289457321167,
+        1.5223047733306885,
+        0.6766162514686584,
+        0.7630574703216553,
+        0.6822458505630493,
+        -0.9668338298797607,
+        0.46929028630256653
+      ],
+      "std": [
+        0.24459987878799438,
+        0.5218791961669922,
+        0.6819084286689758,
+        0.5535831451416016,
+        0.16536164283752441,
+        0.8581108450889587,
+        0.42160049080848694,
+        0.5074564814567566,
+        1.0031490325927734,
+        0.4923613667488098,
+        0.6617483496665955,
+        0.5075437426567078,
+        0.7646470665931702,
+        0.49047353863716125
+      ],
+      "max": [
+        0.08587173372507095,
+        2.7109200954437256,
+        3.1437699794769287,
+        1.352994680404663,
+        0.03168417513370514,
+        1.9688830375671387,
+        1.0,
+        1.2608709335327148,
+        2.8519444465637207,
+        3.266448497772217,
+        1.3512014150619507,
+        1.2738021612167358,
+        0.7937034964561462,
+        1.0
+      ],
+      "min": [
+        -1.1788203716278076,
+        0.0,
+        0.0,
+        -1.865718960762024,
+        -1.1659986972808838,
+        -1.1686975955963135,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.9566882848739624,
+        -0.005439999978989363,
+        -1.559630036354065,
+        0.0
+      ],
+      "q01": [
+        -1.1621215343475342,
+        0.0,
+        0.0,
+        -1.8261551237106324,
+        -1.1501946449279785,
+        -1.1634292602539062,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3071338891983033,
+        -0.004720241297036409,
+        -1.554312481880188,
+        0.0
+      ],
+      "q99": [
+        0.07353582233190536,
+        2.61279296875,
+        2.9014140892028806,
+        1.318111538887024,
+        0.021636107936501503,
+        1.9688830375671387,
+        1.0,
+        1.2599315643310547,
+        2.408996105194092,
+        1.3551753759384155,
+        1.3495060205459595,
+        1.2695887088775635,
+        0.7937034964561462,
+        1.0
+      ]
+    },
+    "num_transitions": 13505,
+    "num_trajectories": 50
+  },
+  "stack_blocks_two_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.21114705502986908,
+        0.9412369132041931,
+        0.7314395904541016,
+        -0.4761776030063629,
+        -0.0009380271076224744,
+        -0.07520589232444763,
+        0.7869701385498047,
+        0.26069143414497375,
+        1.1080588102340698,
+        0.8630656003952026,
+        -0.559351921081543,
+        0.007944311946630478,
+        0.08012033998966217,
+        0.7491208910942078
+      ],
+      "std": [
+        0.3050781488418579,
+        1.0409293174743652,
+        0.8131765127182007,
+        0.5270503759384155,
+        0.008852960541844368,
+        0.4395352900028229,
+        0.3933006227016449,
+        0.3120238780975342,
+        1.047856092453003,
+        0.8200691938400269,
+        0.5292724967002869,
+        0.023433445021510124,
+        0.47746512293815613,
+        0.41560548543930054
+      ],
+      "max": [
+        0.020010676234960556,
+        2.5165865421295166,
+        2.384333372116089,
+        2.1778516384074464e-05,
+        0.10835044085979462,
+        1.2322380542755127,
+        1.0,
+        0.8241801261901855,
+        2.444321393966675,
+        2.2086756229400635,
+        0.0,
+        0.18467359244823456,
+        0.8262989521026611,
+        1.0
+      ],
+      "min": [
+        -0.8325228095054626,
+        -4.1955649976443965e-06,
+        -3.064708653255366e-05,
+        -1.4605095386505127,
+        -0.0383315235376358,
+        -0.8282271027565002,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.7174886781722307e-05,
+        -1.3987765312194824,
+        -0.010218792594969273,
+        -1.1888049840927124,
+        0.0
+      ],
+      "q01": [
+        -0.8252553939819336,
+        0.0,
+        -3.064708653255366e-05,
+        -1.3362677097320557,
+        -0.026790249347686767,
+        -0.8190370202064514,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.340792179107666,
+        -0.006735008861869574,
+        -1.0526111125946045,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.4263905525207523,
+        2.078766918182373,
+        2.1778516384074464e-05,
+        0.019878851249814044,
+        1.0974429607391358,
+        1.0,
+        0.8126579523086548,
+        2.4025102615356446,
+        2.0527486324310305,
+        0.0,
+        0.15595661997795107,
+        0.8210508823394775,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.20990003645420074,
+        0.9378898739814758,
+        0.7284502387046814,
+        -0.47404396533966064,
+        -0.0009204319212585688,
+        -0.07391548156738281,
+        0.7869701385498047,
+        0.25938308238983154,
+        1.1046009063720703,
+        0.8598967790603638,
+        -0.557152271270752,
+        0.007918022572994232,
+        0.07882484793663025,
+        0.7491208910942078
+      ],
+      "std": [
+        0.3042908310890198,
+        1.0406111478805542,
+        0.8124014139175415,
+        0.526269793510437,
+        0.008842087350785732,
+        0.4385771155357361,
+        0.3933006227016449,
+        0.3114229738712311,
+        1.047912836074829,
+        0.8195027112960815,
+        0.5286997556686401,
+        0.023432398214936256,
+        0.47657015919685364,
+        0.41560545563697815
+      ],
+      "max": [
+        0.020010676234960556,
+        2.5165865421295166,
+        2.384333372116089,
+        2.1778516384074464e-05,
+        0.10835044085979462,
+        1.2322380542755127,
+        1.0,
+        0.8241801261901855,
+        2.444321393966675,
+        2.2086756229400635,
+        0.0,
+        0.18467359244823456,
+        0.8262989521026611,
+        1.0
+      ],
+      "min": [
+        -0.8325228095054626,
+        -4.1955649976443965e-06,
+        -3.064708653255366e-05,
+        -1.4605095386505127,
+        -0.0383315235376358,
+        -0.8282271027565002,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.7174886781722307e-05,
+        -1.3987765312194824,
+        -0.010218792594969273,
+        -1.1888049840927124,
+        0.0
+      ],
+      "q01": [
+        -0.8252553939819336,
+        0.0,
+        -3.064708653255366e-05,
+        -1.333071780204773,
+        -0.026790249347686767,
+        -0.8190370202064514,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.3284639596939087,
+        -0.006735008861869574,
+        -1.0526111125946045,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.4263905525207523,
+        2.078766918182373,
+        2.1778516384074464e-05,
+        0.019878851249814044,
+        1.0974429607391358,
+        1.0,
+        0.812503182888031,
+        2.4025102615356446,
+        2.0527486324310305,
+        0.0,
+        0.15595661997795107,
+        0.8210508823394775,
+        1.0
+      ]
+    },
+    "num_transitions": 15641,
+    "num_trajectories": 50
+  },
+  "stack_bowls_two_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.1218431144952774,
+        0.879644513130188,
+        0.6815189719200134,
+        -0.45526257157325745,
+        0.0535038486123085,
+        -0.09123744815587997,
+        0.7937045693397522,
+        0.1586911976337433,
+        1.0623502731323242,
+        0.8378229737281799,
+        -0.5510385036468506,
+        -0.0523577518761158,
+        0.17441710829734802,
+        0.7524454593658447
+      ],
+      "std": [
+        0.20389510691165924,
+        0.9881824851036072,
+        0.7890526652336121,
+        0.5182338953018188,
+        0.10292115807533264,
+        0.15442776679992676,
+        0.3886118233203888,
+        0.2241450399160385,
+        1.0105440616607666,
+        0.8408956527709961,
+        0.5377111434936523,
+        0.10513534396886826,
+        0.2661285400390625,
+        0.41391754150390625
+      ],
+      "max": [
+        0.10931042581796646,
+        2.699620485305786,
+        2.8983492851257324,
+        2.1778516384074464e-05,
+        0.3715807795524597,
+        0.09972000122070312,
+        1.0,
+        0.6297850608825684,
+        3.2168734073638916,
+        3.589359998703003,
+        0.0,
+        0.12872982025146484,
+        1.9198805093765259,
+        1.0
+      ],
+      "min": [
+        -0.583745002746582,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7004367113113403,
+        -0.019333183765411377,
+        -1.0937238931655884,
+        0.0,
+        -0.14522859454154968,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.9368948936462402,
+        -0.35954946279525757,
+        -0.08796866983175278,
+        0.0
+      ],
+      "q01": [
+        -0.5723252892494202,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4582831704616546,
+        -0.0065253665670752525,
+        -0.41102612018585205,
+        0.0,
+        -0.07947887197136878,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.6614161729812622,
+        -0.32789366245269774,
+        -0.06472839415073395,
+        0.0
+      ],
+      "q99": [
+        0.08905640229582787,
+        2.5206500244140626,
+        2.4590754604339606,
+        2.1778516384074464e-05,
+        0.3437314799427987,
+        0.09290612868964686,
+        1.0,
+        0.6060269474983215,
+        2.6554339265823366,
+        2.820633199214936,
+        0.0,
+        0.05726956911385072,
+        0.6783580780029297,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.12101656943559647,
+        0.8765714764595032,
+        0.6787510514259338,
+        -0.453287273645401,
+        0.05318109318614006,
+        -0.09065449237823486,
+        0.7937045693397522,
+        0.15774448215961456,
+        1.0592150688171387,
+        0.8349401950836182,
+        -0.5490079522132874,
+        -0.05208369344472885,
+        0.17349445819854736,
+        0.7524454593658447
+      ],
+      "std": [
+        0.20327353477478027,
+        0.9879177212715149,
+        0.7883735299110413,
+        0.5175690650939941,
+        0.10275426506996155,
+        0.15405894815921783,
+        0.3886118233203888,
+        0.2235718071460724,
+        1.0107218027114868,
+        0.8406001329421997,
+        0.5373188853263855,
+        0.10504182428121567,
+        0.26566997170448303,
+        0.41391754150390625
+      ],
+      "max": [
+        0.10931042581796646,
+        2.699620485305786,
+        2.8983492851257324,
+        2.1778516384074464e-05,
+        0.3715807795524597,
+        0.09972000122070312,
+        1.0,
+        0.6297850608825684,
+        3.2168734073638916,
+        3.589359998703003,
+        0.0,
+        0.12872982025146484,
+        1.9198805093765259,
+        1.0
+      ],
+      "min": [
+        -0.583745002746582,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7004367113113403,
+        -0.019333183765411377,
+        -1.0937238931655884,
+        0.0,
+        -0.14522859454154968,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.9368948936462402,
+        -0.35954946279525757,
+        -0.08796866983175278,
+        0.0
+      ],
+      "q01": [
+        -0.5723252892494202,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4582276344299316,
+        -0.0065253665670752525,
+        -0.41102612018585205,
+        0.0,
+        -0.07947887197136878,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.6614161729812622,
+        -0.32789366245269774,
+        -0.06472839415073395,
+        0.0
+      ],
+      "q99": [
+        0.08905640229582787,
+        2.5206500244140626,
+        2.4590754604339606,
+        2.1778516384074464e-05,
+        0.3437314799427987,
+        0.09290612868964686,
+        1.0,
+        0.6060269474983215,
+        2.6554339265823366,
+        2.820633199214936,
+        0.0,
+        0.05726956911385072,
+        0.6783580780029297,
+        1.0
+      ]
+    },
+    "num_transitions": 15948,
+    "num_trajectories": 50
+  }
+}

results/simvla_fredf_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_fredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M2000-F2000-D1000--2000_chkpt/dataset_statistics.json

@@ -0,0 +1,218 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  }
+}

results/simvla_fredf_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_fredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M2000-F2000-D1000--2000_chkpt/lora_adapter/README.md

@@ -0,0 +1,202 @@
+---
+base_model: /inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/ai_models/openvla/openvla-7b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1

results/simvla_fredf_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_fredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M2000-F2000-D1000--2000_chkpt/processing_prismatic.py

@@ -0,0 +1,257 @@
+"""
+processing_prismatic.py
+
+HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
+specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, ClassVar, List, Optional, Tuple, Union
+
+import timm.data
+import torch
+import torchvision.transforms.functional as TVF
+from PIL import Image
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from transformers import PreTrainedTokenizerBase
+from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+# === Image Processing ===
+def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
+    """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
+    (w, h), max_wh = image.size, max(image.size)
+    horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
+    padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
+
+    return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
+
+
+class PrismaticImageProcessor(ImageProcessingMixin):
+    model_input_names: ClassVar[List[str]] = ["pixel_values"]
+
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool = False,
+        image_resize_strategy: str = "letterbox",
+        input_sizes: Optional[List[Tuple[int, int, int]]] = None,
+        interpolations: Optional[List[str]] = None,
+        means: Optional[List[Tuple[float, float, float]]] = None,
+        stds: Optional[List[Tuple[float, float, float]]] = None,
+        **kwargs: str,
+    ) -> None:
+        """
+        Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
+        created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
+
+        @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
+        @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
+        @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
+        @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
+        @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
+        @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
+        """
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.image_resize_strategy = image_resize_strategy
+
+        # Handle `None` default values
+        input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
+        means = [(0.5, 0.5, 0.5)] if means is None else means
+        stds = [(0.5, 0.5, 0.5)] if stds is None else stds
+
+        # TIMM `data_cfg` Parameters
+        self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
+
+        # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
+        self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
+        self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+        for idx in range(len(input_sizes)):
+            transform = timm.data.create_transform(
+                input_size=self.input_sizes[idx],
+                interpolation=self.interpolations[idx],
+                mean=self.means[idx],
+                std=self.stds[idx],
+                crop_pct=1.0,  # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
+                crop_mode="center",  # Default crop mode -- no-op when `crop_pct == 1.0`
+                is_training=False,  # No image augmentations when loading the transform!
+            )
+
+            # [Validation] Ensure appropriate transform structure, expected sizes
+            if not (
+                isinstance(transform, Compose)
+                and (len(transform.transforms) == 4)
+                and isinstance(transform.transforms[0], Resize)
+                and isinstance(transform.transforms[1], CenterCrop)
+                and isinstance(transform.transforms[2], ToTensor)
+                and isinstance(transform.transforms[3], Normalize)
+                and (transform.transforms[0].size == self.input_sizes[idx][-1])
+                and (transform.transforms[1].size == self.input_sizes[idx][-2:])
+            ):
+                raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
+
+            # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
+            #   => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
+            resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
+            self.tvf_resize_params.append(
+                {
+                    "size": resize_t.size,
+                    "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
+                    "max_size": None,
+                    "antialias": True,
+                }
+            )
+            self.tvf_crop_params.append({"output_size": crop_t.size})
+            self.tvf_normalize_params.append(
+                {
+                    "mean": norm_t.mean.float().numpy().tolist(),
+                    "std": norm_t.std.float().numpy().tolist(),
+                    "inplace": False,
+                }
+            )
+            self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+            # Handle Prismatic `image_resize_strategy`
+            if self.image_resize_strategy == "resize-naive":
+                self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
+            elif self.image_resize_strategy == "letterbox":
+                self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
+            elif self.image_resize_strategy == "resize-crop":
+                pass
+            else:
+                raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
+
+        # Dispatch **kwargs to super()
+        super().__init__(**kwargs)
+
+    def apply_transform(self, img: Image.Image) -> torch.Tensor:
+        """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
+        if self.tvf_do_letterbox:
+            img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
+
+        # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
+        imgs_t = []
+        for idx in range(len(self.input_sizes)):
+            img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
+            img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
+            img_idx_t = TVF.to_tensor(img_idx)
+            img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
+            imgs_t.append(img_idx_t)
+
+        # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
+        img_t = torch.vstack(imgs_t)
+
+        return img_t
+
+    def preprocess(
+        self,
+        images: Union[Image.Image, List[Image.Image]],
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **_: str,
+    ) -> BatchFeature:
+        """
+        Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
+        explicitly only handle PIL.Image.Image instances for simplicity.
+
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
+
+        @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
+        """
+        if not isinstance(images, list):
+            images = [images]
+
+        # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
+        pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
+
+        # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
+        return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
+
+    def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
+        return self.preprocess(images, **kwargs)
+
+
+# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
+#   =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
+class PrismaticProcessor(ProcessorMixin):
+    attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
+    image_processor_class: str = "AutoImageProcessor"
+    tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: Optional[ImageProcessingMixin] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+    ) -> None:
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        images: Union[Image.Image, List[Image.Image]],
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
+        forwards images to PrismaticImageProcessor.
+
+        @param text: The (batch) of text to encode; must be a string or list of strings.
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
+        @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
+        @param max_length: Maximum length (in tokens) to truncate
+        @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
+
+        @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
+        """
+        pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
+        text_inputs = self.tokenizer(
+            text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
+        )
+
+        # [Validate] Need same number of images and text inputs!
+        if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
+            raise ValueError("Batch is malformed; expected same number of images and text inputs!")
+
+        return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
+
+    # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
+    def batch_decode(
+        self,
+        sequences: Union[List[int], List[List[int]], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> List[str]:
+        return self.tokenizer.batch_decode(
+            sequences=sequences,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def decode(
+        self,
+        token_ids: Union[int, List[int], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> str:
+        return self.tokenizer.decode(
+            token_ids=token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self) -> List[str]:
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

results/simvla_fredf_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_fredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M2000-F2000-D1000--2000_chkpt/processor_config.json

@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "processor_class": "PrismaticProcessor"
+}

results/simvla_fredf_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_fredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M2000-F2000-D1000--2000_chkpt/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

results/simvla_fredf_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_fredf_25_inner1_proj_type_gelu_linear_ffn_type_gelu_mlp_ffn_decoder_num_blocks_2-M2000-F2000-D1000--2000_chkpt/tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--10000_chkpt/dataset_statistics.json

@@ -0,0 +1,2810 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  },
+  "handover_mic_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.3111798167228699,
+        0.9980528354644775,
+        0.5811634659767151,
+        0.23248183727264404,
+        0.1577642261981964,
+        -0.023892972618341446,
+        0.607450008392334,
+        0.37389707565307617,
+        1.1807876825332642,
+        0.6784368753433228,
+        0.25683099031448364,
+        -0.1075415387749672,
+        0.08219857513904572,
+        0.5450003743171692
+      ],
+      "std": [
+        0.2626854181289673,
+        0.7778211832046509,
+        0.46585971117019653,
+        0.5015608668327332,
+        0.2322310358285904,
+        0.10976110398769379,
+        0.4736306667327881,
+        0.2574075758457184,
+        0.7576491832733154,
+        0.4595378041267395,
+        0.5935487747192383,
+        0.16007103025913239,
+        0.14593657851219177,
+        0.4819530248641968
+      ],
+      "max": [
+        0.0,
+        2.1762497425079346,
+        1.5628178119659424,
+        0.990909993648529,
+        0.8559104800224304,
+        0.11719957739114761,
+        1.0,
+        0.837027370929718,
+        2.206538200378418,
+        1.6232832670211792,
+        1.1574815511703491,
+        0.2886713147163391,
+        0.5811867117881775,
+        1.0
+      ],
+      "min": [
+        -0.8862171173095703,
+        0.0,
+        0.0,
+        -1.0514172315597534,
+        -0.39507749676704407,
+        -0.4001063406467438,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0771245956420898,
+        -0.8672545552253723,
+        -0.07416637241840363,
+        0.0
+      ],
+      "q01": [
+        -0.7671213400363922,
+        0.0,
+        0.0,
+        -0.9784595704078675,
+        -0.27155110239982605,
+        -0.3553581619262695,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0119924187660216,
+        -0.651338517665863,
+        -0.06671902537345886,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.131481647491455,
+        1.404119849205017,
+        0.9854996800422668,
+        0.7671356797218323,
+        0.1121677927672863,
+        1.0,
+        0.7132604718208313,
+        2.139058828353882,
+        1.4547646927833575,
+        1.1053520989418035,
+        0.1410233974456787,
+        0.4914003610610962,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.30907687544822693,
+        0.9918185472488403,
+        0.5777602791786194,
+        0.22962018847465515,
+        0.15603166818618774,
+        -0.02409629337489605,
+        0.610062837600708,
+        0.37142202258110046,
+        1.1745105981826782,
+        0.6752545237541199,
+        0.2535216212272644,
+        -0.10635410249233246,
+        0.08214741945266724,
+        0.546892523765564
+      ],
+      "std": [
+        0.26320481300354004,
+        0.7802274823188782,
+        0.4670981466770172,
+        0.5009189248085022,
+        0.23124119639396667,
+        0.10963220149278641,
+        0.47306305170059204,
+        0.2583112120628357,
+        0.7615904211997986,
+        0.4614890217781067,
+        0.5927368402481079,
+        0.15948225557804108,
+        0.14593760669231415,
+        0.4817669689655304
+      ],
+      "max": [
+        0.0,
+        2.1762497425079346,
+        1.5628178119659424,
+        0.990909993648529,
+        0.8559104800224304,
+        0.11719957739114761,
+        1.0,
+        0.837027370929718,
+        2.206538200378418,
+        1.6232832670211792,
+        1.1574815511703491,
+        0.2886713147163391,
+        0.5811867117881775,
+        1.0
+      ],
+      "min": [
+        -0.8862171173095703,
+        0.0,
+        0.0,
+        -1.0514172315597534,
+        -0.39507749676704407,
+        -0.4001063406467438,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0771245956420898,
+        -0.8672545552253723,
+        -0.07416637241840363,
+        0.0
+      ],
+      "q01": [
+        -0.7671213400363922,
+        0.0,
+        0.0,
+        -0.9784595704078675,
+        -0.27155110239982605,
+        -0.3553581619262695,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0119924187660216,
+        -0.651338517665863,
+        -0.06671902537345886,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.131481647491455,
+        1.404119849205017,
+        0.9854996800422668,
+        0.7671356797218323,
+        0.11196876823902131,
+        1.0,
+        0.7132604718208313,
+        2.139058828353882,
+        1.4547646927833575,
+        1.0914406299591126,
+        0.1410233974456787,
+        0.4914003610610962,
+        1.0
+      ]
+    },
+    "num_transitions": 11099,
+    "num_trajectories": 50
+  },
+  "lift_pot_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.08297546207904816,
+        1.409603238105774,
+        0.9623388648033142,
+        -0.41438642144203186,
+        0.6548429131507874,
+        -0.1665104776620865,
+        0.3988376259803772,
+        0.07074239104986191,
+        1.438857078552246,
+        1.0271384716033936,
+        -0.4811238646507263,
+        -0.6411229968070984,
+        0.13417655229568481,
+        0.3988376259803772
+      ],
+      "std": [
+        0.07541920989751816,
+        0.9829710721969604,
+        0.7201086282730103,
+        0.4319354295730591,
+        0.44529980421066284,
+        0.2749307453632355,
+        0.25010964274406433,
+        0.07464443892240524,
+        1.0042012929916382,
+        0.7712593078613281,
+        0.46114370226860046,
+        0.4362685978412628,
+        0.2527497708797455,
+        0.25010964274406433
+      ],
+      "max": [
+        0.044971514493227005,
+        2.7116494178771973,
+        2.7667176723480225,
+        0.16915999352931976,
+        1.0692775249481201,
+        0.3872540295124054,
+        1.0,
+        0.3003857135772705,
+        2.6776700019836426,
+        2.6789329051971436,
+        0.1484600007534027,
+        0.0,
+        0.8275586366653442,
+        1.0
+      ],
+      "min": [
+        -0.2611783444881439,
+        0.0,
+        0.0,
+        -1.7497934103012085,
+        0.0,
+        -0.7937148809432983,
+        0.0,
+        -0.036408890038728714,
+        0.0,
+        0.0,
+        -1.7768168449401855,
+        -1.0369138717651367,
+        -0.34945493936538696,
+        0.0
+      ],
+      "q01": [
+        -0.24835357069969177,
+        0.0,
+        0.0,
+        -1.4652207350730897,
+        0.0,
+        -0.7823562324047089,
+        0.0,
+        -0.02311648927628994,
+        0.0,
+        0.0,
+        -1.5518183708190918,
+        -1.0312373971939086,
+        -0.27689045667648315,
+        0.0
+      ],
+      "q99": [
+        0.018272288143634796,
+        2.6518030166625977,
+        2.3128650331497194,
+        0.1131945550441742,
+        1.0648717546463013,
+        0.30490371704101565,
+        0.9623115658760071,
+        0.2519652831554413,
+        2.6547678661346437,
+        2.426756353378296,
+        0.004153958521783352,
+        0.0,
+        0.7562372756004334,
+        0.9623115658760071
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.08176180720329285,
+        1.390768051147461,
+        0.9472193121910095,
+        -0.4064285159111023,
+        0.6457239389419556,
+        -0.1636435091495514,
+        0.4078385531902313,
+        0.06947626918554306,
+        1.4194576740264893,
+        1.0109381675720215,
+        -0.47207584977149963,
+        -0.6321477890014648,
+        0.1318783462047577,
+        0.4078385531902313
+      ],
+      "std": [
+        0.07548711448907852,
+        0.9894453287124634,
+        0.7214877009391785,
+        0.4296827018260956,
+        0.44820061326026917,
+        0.2734803557395935,
+        0.253564715385437,
+        0.074403315782547,
+        1.0106186866760254,
+        0.7725314497947693,
+        0.4590045213699341,
+        0.4390813708305359,
+        0.2512795925140381,
+        0.253564715385437
+      ],
+      "max": [
+        0.044971514493227005,
+        2.7116494178771973,
+        2.7667176723480225,
+        0.16915999352931976,
+        1.0692775249481201,
+        0.3872540295124054,
+        1.0,
+        0.30037084221839905,
+        2.6776700019836426,
+        2.6789329051971436,
+        0.1484600007534027,
+        0.0,
+        0.8275586366653442,
+        1.0
+      ],
+      "min": [
+        -0.2611783444881439,
+        0.0,
+        0.0,
+        -1.7497934103012085,
+        0.0,
+        -0.7937148809432983,
+        0.0,
+        -0.036408890038728714,
+        0.0,
+        0.0,
+        -1.7768168449401855,
+        -1.0369138717651367,
+        -0.34945493936538696,
+        0.0
+      ],
+      "q01": [
+        -0.24835357069969177,
+        0.0,
+        0.0,
+        -1.4640827035903932,
+        0.0,
+        -0.7821481037139892,
+        0.0,
+        -0.02311648927628994,
+        0.0,
+        0.0,
+        -1.5469084978103638,
+        -1.0312373971939086,
+        -0.27689045667648315,
+        0.0
+      ],
+      "q99": [
+        0.018272288143634796,
+        2.6518030166625977,
+        2.2994370603561403,
+        0.1131945550441742,
+        1.0648064422607422,
+        0.30490371704101565,
+        1.0,
+        0.2516305303573608,
+        2.654694414138794,
+        2.3750886774063114,
+        0.004153958521783352,
+        0.0,
+        0.756147563457489,
+        1.0
+      ]
+    },
+    "num_transitions": 5555,
+    "num_trajectories": 50
+  },
+  "move_can_pot_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.03227013349533081,
+        0.9706242084503174,
+        0.8449752926826477,
+        -0.4254174828529358,
+        0.05570398271083832,
+        0.09763211756944656,
+        0.7630919218063354,
+        0.043113451451063156,
+        1.036581039428711,
+        0.9449251294136047,
+        -0.5384683012962341,
+        -0.01411368977278471,
+        -0.09070954471826553,
+        0.7476586103439331
+      ],
+      "std": [
+        0.11224070936441422,
+        1.1117055416107178,
+        1.0112414360046387,
+        0.6318230628967285,
+        0.18493546545505524,
+        0.2674776613712311,
+        0.40844425559043884,
+        0.10649054497480392,
+        1.1204936504364014,
+        1.0428167581558228,
+        0.589298665523529,
+        0.058494143187999725,
+        0.27779391407966614,
+        0.41657936573028564
+      ],
+      "max": [
+        0.3149413466453552,
+        3.0223748683929443,
+        3.342350721359253,
+        1.4929821491241455,
+        1.153774380683899,
+        0.9257752299308777,
+        1.0,
+        0.32490795850753784,
+        2.995034694671631,
+        3.3333518505096436,
+        0.0,
+        0.11092565208673477,
+        0.318450003862381,
+        1.0
+      ],
+      "min": [
+        -0.3494536280632019,
+        0.0,
+        0.0,
+        -1.7953904867172241,
+        -0.13677525520324707,
+        -0.3176078498363495,
+        0.0,
+        -0.0804772898554802,
+        0.0,
+        0.0,
+        -1.8121548891067505,
+        -0.21740999817848206,
+        -0.8863733410835266,
+        0.0
+      ],
+      "q01": [
+        -0.3341374397277832,
+        0.0,
+        0.0,
+        -1.6472991704940796,
+        -0.11959030479192734,
+        -0.30239319801330566,
+        0.0,
+        -0.0527563564479351,
+        0.0,
+        0.0,
+        -1.6625858545303345,
+        -0.19942482709884643,
+        -0.830798065662384,
+        0.0
+      ],
+      "q99": [
+        0.2560995936393735,
+        2.8609605312347384,
+        3.1173167228698704,
+        1.3075437545776367,
+        1.0580827236175536,
+        0.9104878306388855,
+        1.0,
+        0.31480422616004944,
+        2.913422060012817,
+        3.2138250827789308,
+        0.0,
+        0.09690605103969574,
+        0.31249096989631653,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.03136930614709854,
+        0.9640669822692871,
+        0.8403865694999695,
+        -0.423330694437027,
+        0.0556751973927021,
+        0.09843266755342484,
+        0.7630919218063354,
+        0.042088694870471954,
+        1.0294307470321655,
+        0.939507782459259,
+        -0.5354413986206055,
+        -0.014315903186798096,
+        -0.09174654632806778,
+        0.7476586103439331
+      ],
+      "std": [
+        0.11126016825437546,
+        1.1112853288650513,
+        1.0116392374038696,
+        0.6310778856277466,
+        0.1844586580991745,
+        0.2667086720466614,
+        0.40844425559043884,
+        0.10545298457145691,
+        1.1204065084457397,
+        1.0435622930526733,
+        0.5897011756896973,
+        0.05830603092908859,
+        0.2768862843513489,
+        0.41657936573028564
+      ],
+      "max": [
+        0.3149413466453552,
+        3.0223748683929443,
+        3.342350721359253,
+        1.4929821491241455,
+        1.153774380683899,
+        0.9257752299308777,
+        1.0,
+        0.32490795850753784,
+        2.995034694671631,
+        3.3333518505096436,
+        0.0,
+        0.11092565208673477,
+        0.318450003862381,
+        1.0
+      ],
+      "min": [
+        -0.3494536280632019,
+        0.0,
+        0.0,
+        -1.7953904867172241,
+        -0.13677525520324707,
+        -0.3176078498363495,
+        0.0,
+        -0.0804772898554802,
+        0.0,
+        0.0,
+        -1.8121548891067505,
+        -0.21740999817848206,
+        -0.8863733410835266,
+        0.0
+      ],
+      "q01": [
+        -0.3341374397277832,
+        0.0,
+        0.0,
+        -1.6472991704940796,
+        -0.11959030479192734,
+        -0.30239319801330566,
+        0.0,
+        -0.0527563564479351,
+        0.0,
+        0.0,
+        -1.6625858545303345,
+        -0.19942482709884643,
+        -0.830798065662384,
+        0.0
+      ],
+      "q99": [
+        0.2560995936393735,
+        2.8609605312347384,
+        3.1173167228698704,
+        1.3057452917098997,
+        1.0580827236175536,
+        0.9104878306388855,
+        1.0,
+        0.31480422616004944,
+        2.913422060012817,
+        3.2138250827789308,
+        0.0,
+        0.09655882418155662,
+        0.3124178946018219,
+        1.0
+      ]
+    },
+    "num_transitions": 7581,
+    "num_trajectories": 50
+  },
+  "open_laptop_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.412982702255249,
+        0.9725632071495056,
+        0.5560179352760315,
+        -0.018646035343408585,
+        -0.1403660625219345,
+        0.671284019947052,
+        0.5757868885993958,
+        0.3011881411075592,
+        0.795698344707489,
+        0.4804641604423523,
+        -0.04225534945726395,
+        0.059194255620241165,
+        0.7393441200256348,
+        0.648875892162323
+      ],
+      "std": [
+        0.41707736253738403,
+        0.9429622888565063,
+        0.6425602436065674,
+        0.4222852289676666,
+        0.2817792296409607,
+        0.6755929589271545,
+        0.48893213272094727,
+        0.3684118092060089,
+        0.9421394467353821,
+        0.6803411245346069,
+        0.4267505407333374,
+        0.22786928713321686,
+        0.8749793171882629,
+        0.47337010502815247
+      ],
+      "max": [
+        0.014050900936126709,
+        2.7143473625183105,
+        2.9012439250946045,
+        1.1832900047302246,
+        0.8149839043617249,
+        2.0226521492004395,
+        1.0,
+        1.3892728090286255,
+        2.72947359085083,
+        2.906212091445923,
+        1.3124799728393555,
+        1.0027532577514648,
+        2.6277825832366943,
+        1.0
+      ],
+      "min": [
+        -1.3947231769561768,
+        0.0,
+        0.0,
+        -1.5809979438781738,
+        -1.1472011804580688,
+        0.0,
+        0.0,
+        -0.005201177205890417,
+        0.0,
+        -0.009437700733542442,
+        -1.6318897008895874,
+        -0.7902398705482483,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -1.213874478340149,
+        0.0,
+        0.0,
+        -1.27146577835083,
+        -0.886477215886116,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3470568656921387,
+        -0.5466296076774597,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.417337417602539,
+        2.35109806060791,
+        0.9501821488142007,
+        0.5932847791910171,
+        1.9652117490768433,
+        1.0,
+        1.1480320692062378,
+        2.519510269165039,
+        2.53169584274292,
+        1.0674813234806055,
+        0.8172210627794251,
+        2.301086187362671,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.41133424639701843,
+        0.9674974679946899,
+        0.5518006682395935,
+        -0.016732074320316315,
+        -0.1401025503873825,
+        0.6682498455047607,
+        0.5781423449516296,
+        0.30009010434150696,
+        0.7918621301651001,
+        0.4770472049713135,
+        -0.040619634091854095,
+        0.059146538376808167,
+        0.7364427447319031,
+        0.6505815386772156
+      ],
+      "std": [
+        0.41728851199150085,
+        0.9423418641090393,
+        0.6399604678153992,
+        0.4200330376625061,
+        0.2817077338695526,
+        0.6755438446998596,
+        0.48856765031814575,
+        0.36834537982940674,
+        0.9407066106796265,
+        0.6772210597991943,
+        0.424578994512558,
+        0.22778856754302979,
+        0.8745430111885071,
+        0.47281336784362793
+      ],
+      "max": [
+        0.014050900936126709,
+        2.7143473625183105,
+        2.9012439250946045,
+        1.1832900047302246,
+        0.8149839043617249,
+        2.0226521492004395,
+        1.0,
+        1.3892728090286255,
+        2.72947359085083,
+        2.906212091445923,
+        1.3124799728393555,
+        1.0027532577514648,
+        2.6277825832366943,
+        1.0
+      ],
+      "min": [
+        -1.3947231769561768,
+        0.0,
+        0.0,
+        -1.5809979438781738,
+        -1.1472011804580688,
+        0.0,
+        0.0,
+        -0.005201177205890417,
+        0.0,
+        -0.009437700733542442,
+        -1.6318897008895874,
+        -0.7902398705482483,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -1.213874478340149,
+        0.0,
+        0.0,
+        -1.27146577835083,
+        -0.886477215886116,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3470568656921387,
+        -0.5466296076774597,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.417337417602539,
+        2.35109806060791,
+        0.9501821488142007,
+        0.5932847791910171,
+        1.9652117490768433,
+        1.0,
+        1.1480320692062378,
+        2.519510269165039,
+        2.53169584274292,
+        1.0674813234806055,
+        0.8172210627794251,
+        2.301086187362671,
+        1.0
+      ]
+    },
+    "num_transitions": 12312,
+    "num_trajectories": 50
+  },
+  "pick_dual_bottles_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.15950368344783783,
+        1.7896647453308105,
+        0.9439552426338196,
+        0.7748593091964722,
+        0.5115674138069153,
+        0.05964143946766853,
+        0.49804794788360596,
+        0.1409446895122528,
+        1.8259799480438232,
+        1.0187904834747314,
+        0.7640450596809387,
+        -0.5400498509407043,
+        -0.017349114641547203,
+        0.49804794788360596
+      ],
+      "std": [
+        0.20802833139896393,
+        0.7114025950431824,
+        0.49483397603034973,
+        0.31811821460723877,
+        0.31533852219581604,
+        0.07191983610391617,
+        0.48165053129196167,
+        0.2233102172613144,
+        0.717391312122345,
+        0.5089762806892395,
+        0.29336756467819214,
+        0.3482920825481415,
+        0.050167474895715714,
+        0.48165053129196167
+      ],
+      "max": [
+        0.39062753319740295,
+        2.8961520195007324,
+        2.413680076599121,
+        1.6144700050354004,
+        1.4948179721832275,
+        0.6830681562423706,
+        1.0,
+        0.6129000186920166,
+        2.735783815383911,
+        2.0820229053497314,
+        1.28603994846344,
+        0.0,
+        0.05678669363260269,
+        1.0
+      ],
+      "min": [
+        -0.5722799897193909,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.04302142560482025,
+        0.0,
+        -0.3316712975502014,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.37959623336792,
+        -0.302682101726532,
+        0.0
+      ],
+      "q01": [
+        -0.5586331933736801,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.010444268118590117,
+        0.0,
+        -0.26194658875465393,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.2651588320732117,
+        -0.17471149563789368,
+        0.0
+      ],
+      "q99": [
+        0.2784392535686493,
+        2.7243048548698425,
+        1.9894878566265106,
+        1.431083768606186,
+        1.2972519397735596,
+        0.3371240422129631,
+        1.0,
+        0.5960070490837097,
+        2.733114242553711,
+        2.0299019813537598,
+        1.236779510974884,
+        0.0,
+        0.04197721276432276,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.15498977899551392,
+        1.7792388200759888,
+        0.9396310448646545,
+        0.769199013710022,
+        0.5095281600952148,
+        0.0594169907271862,
+        0.5062099099159241,
+        0.13609662652015686,
+        1.814998984336853,
+        1.0140470266342163,
+        0.7581976652145386,
+        -0.538648784160614,
+        -0.01742432452738285,
+        0.5062099099159241
+      ],
+      "std": [
+        0.20542342960834503,
+        0.7279935479164124,
+        0.5007017254829407,
+        0.32545188069343567,
+        0.3178229331970215,
+        0.07205121219158173,
+        0.4816075265407562,
+        0.21984270215034485,
+        0.7347503900527954,
+        0.5156906247138977,
+        0.3011578619480133,
+        0.35011228919029236,
+        0.050126101821660995,
+        0.4816075265407562
+      ],
+      "max": [
+        0.39062753319740295,
+        2.8961520195007324,
+        2.413680076599121,
+        1.6144700050354004,
+        1.4948179721832275,
+        0.6830681562423706,
+        1.0,
+        0.6129000186920166,
+        2.735783815383911,
+        2.0820229053497314,
+        1.28603994846344,
+        0.0,
+        0.05678669363260269,
+        1.0
+      ],
+      "min": [
+        -0.5722799897193909,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -0.04302142560482025,
+        0.0,
+        -0.3316712975502014,
+        0.0,
+        0.0,
+        0.0,
+        -1.37959623336792,
+        -0.302682101726532,
+        0.0
+      ],
+      "q01": [
+        -0.5585764646530151,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.010444268118590117,
+        0.0,
+        -0.26194658875465393,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.2651588320732117,
+        -0.17471149563789368,
+        0.0
+      ],
+      "q99": [
+        0.2784392535686493,
+        2.7243048548698425,
+        1.9894878566265106,
+        1.431083768606186,
+        1.2972519397735596,
+        0.3371240422129631,
+        1.0,
+        0.5959392786026001,
+        2.733114242553711,
+        2.0299019813537598,
+        1.236779510974884,
+        0.0,
+        0.04197721276432276,
+        1.0
+      ]
+    },
+    "num_transitions": 6126,
+    "num_trajectories": 50
+  },
+  "place_dual_shoes_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.364918053150177,
+        1.520833969116211,
+        1.0176814794540405,
+        -0.6050214767456055,
+        0.016822682693600655,
+        -0.1008746474981308,
+        0.6267212629318237,
+        0.1734830141067505,
+        1.9947360754013062,
+        1.4386961460113525,
+        -0.9159395098686218,
+        -0.003797657787799835,
+        -0.27571171522140503,
+        0.31392714381217957
+      ],
+      "std": [
+        1.027938961982727,
+        0.9228556752204895,
+        0.6396123766899109,
+        0.3982831537723541,
+        0.05350078269839287,
+        1.1809768676757812,
+        0.4633759558200836,
+        0.302899032831192,
+        0.5275591015815735,
+        0.43367037177085876,
+        0.289938747882843,
+        0.02490071952342987,
+        1.0572752952575684,
+        0.44288790225982666
+      ],
+      "max": [
+        5.369949817657471,
+        2.5988383293151855,
+        2.2322678565979004,
+        2.1778516384074464e-05,
+        0.27490007877349854,
+        3.83882999420166,
+        1.0,
+        0.7725619673728943,
+        2.6282360553741455,
+        2.372544288635254,
+        0.0,
+        0.07164999842643738,
+        2.342289924621582,
+        1.0
+      ],
+      "min": [
+        -7.180570602416992,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4090766906738281,
+        -0.10993228107690811,
+        -2.4864256381988525,
+        0.0,
+        -0.2831000089645386,
+        0.0,
+        0.0,
+        -1.5554989576339722,
+        -0.13825319707393646,
+        -4.031702518463135,
+        0.0
+      ],
+      "q01": [
+        -6.867993459701538,
+        2.9715695291088196e-06,
+        -3.064708653255366e-05,
+        -1.2694409608840942,
+        -0.06486005619168282,
+        -2.466008424758911,
+        0.0,
+        -0.26483604311943054,
+        0.009487488549202682,
+        0.009014308238402009,
+        -1.411148397922516,
+        -0.11009304970502853,
+        -3.9720680713653564,
+        0.0
+      ],
+      "q99": [
+        1.2002487826347954,
+        2.494779109954834,
+        1.9488718807697298,
+        2.1778516384074464e-05,
+        0.2206193839013577,
+        3.605363287925731,
+        1.0,
+        0.7506802678108215,
+        2.5776290893554688,
+        2.192576406002045,
+        -0.005188394398428484,
+        0.057537742741406016,
+        2.3226377964019775,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.3650052845478058,
+        1.520833969116211,
+        1.0176814794540405,
+        -0.6050214767456055,
+        0.016822639852762222,
+        -0.1008746474981308,
+        0.6267212629318237,
+        0.17031559348106384,
+        1.9842966794967651,
+        1.4307619333267212,
+        -0.9114848971366882,
+        -0.0037371169310063124,
+        -0.2758965492248535,
+        0.31392714381217957
+      ],
+      "std": [
+        1.0279076099395752,
+        0.9228557348251343,
+        0.6396121978759766,
+        0.3982830345630646,
+        0.05350079387426376,
+        1.1809768676757812,
+        0.4633759558200836,
+        0.30088669061660767,
+        0.5429739952087402,
+        0.4430801570415497,
+        0.29594969749450684,
+        0.02473057620227337,
+        1.0498296022415161,
+        0.44288793206214905
+      ],
+      "max": [
+        5.369949817657471,
+        2.5988383293151855,
+        2.2322678565979004,
+        2.1778516384074464e-05,
+        0.27490007877349854,
+        3.83882999420166,
+        1.0,
+        0.7725619673728943,
+        2.6282360553741455,
+        2.372544288635254,
+        0.0,
+        0.07164999842643738,
+        2.342289924621582,
+        1.0
+      ],
+      "min": [
+        -7.180570602416992,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4090766906738281,
+        -0.10993228107690811,
+        -2.4864256381988525,
+        0.0,
+        -0.2831000089645386,
+        0.0,
+        0.0,
+        -1.5554989576339722,
+        -0.13825319707393646,
+        -4.031702518463135,
+        0.0
+      ],
+      "q01": [
+        -6.867993459701538,
+        0.0,
+        -3.064708653255366e-05,
+        -1.2694409608840942,
+        -0.06486005619168282,
+        -2.466008424758911,
+        0.0,
+        -0.26483604311943054,
+        0.0,
+        0.0,
+        -1.411148397922516,
+        -0.11009304970502853,
+        -3.9709057807922363,
+        0.0
+      ],
+      "q99": [
+        1.2002487826347954,
+        2.494779109954834,
+        1.9488718807697298,
+        2.1778516384074464e-05,
+        0.2206193839013577,
+        3.605363287925731,
+        1.0,
+        0.7506802678108215,
+        2.5776290893554688,
+        2.192576406002045,
+        0.0,
+        0.056274176910520335,
+        2.3226377964019775,
+        1.0
+      ]
+    },
+    "num_transitions": 11474,
+    "num_trajectories": 50
+  },
+  "place_object_basket_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.17315083742141724,
+        1.1054847240447998,
+        0.9055297374725342,
+        -0.3234107792377472,
+        0.21469952166080475,
+        0.17225050926208496,
+        0.7572181820869446,
+        0.16253036260604858,
+        0.9573038816452026,
+        0.8219836950302124,
+        -0.3597754240036011,
+        -0.10522812604904175,
+        -0.17085255682468414,
+        0.7928103804588318
+      ],
+      "std": [
+        0.21726401150226593,
+        1.0674771070480347,
+        0.9510499238967896,
+        0.7475572824478149,
+        0.4299476444721222,
+        0.44102591276168823,
+        0.4120478928089142,
+        0.3323862552642822,
+        1.0525022745132446,
+        0.9476361870765686,
+        0.7507379055023193,
+        0.3281928598880768,
+        0.524770200252533,
+        0.38940587639808655
+      ],
+      "max": [
+        0.02667570859193802,
+        3.778841733932495,
+        4.568304061889648,
+        1.0729998350143433,
+        1.2834042310714722,
+        1.6689540147781372,
+        1.0,
+        0.8685938715934753,
+        2.6522276401519775,
+        3.264349937438965,
+        1.1015950441360474,
+        1.1631274223327637,
+        3.160046100616455,
+        1.0
+      ],
+      "min": [
+        -0.858056902885437,
+        0.0,
+        -3.064708653255366e-05,
+        -1.8265033960342407,
+        -0.028474153950810432,
+        -0.8472005128860474,
+        0.0,
+        -2.803896188735962,
+        -0.4660651385784149,
+        -2.5819708753260784e-05,
+        -1.9893873929977417,
+        -1.1286083459854126,
+        -1.736908197402954,
+        0.0
+      ],
+      "q01": [
+        -0.6967605352401733,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7222540175914764,
+        -0.014083670675754547,
+        -0.5925237935781479,
+        0.0,
+        -0.04488968480378389,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7749713563919067,
+        -1.1208465099334717,
+        -1.7055805158615112,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.777959144115448,
+        2.902973175048828,
+        1.0099876379966732,
+        1.272336483001709,
+        1.5119220542907714,
+        1.0,
+        0.8364987969398499,
+        2.5737154483795166,
+        2.664341926574707,
+        1.0096371030807485,
+        0.11334303751587624,
+        0.8461964726448059,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.17225174605846405,
+        1.101753830909729,
+        0.9016473889350891,
+        -0.3206050992012024,
+        0.21468140184879303,
+        0.17057755589485168,
+        0.7589125037193298,
+        0.1611548811197281,
+        0.9520825743675232,
+        0.8164799809455872,
+        -0.35582447052001953,
+        -0.10522229224443436,
+        -0.16855837404727936,
+        0.7951502203941345
+      ],
+      "std": [
+        0.21675990521907806,
+        1.0675125122070312,
+        0.9500465393066406,
+        0.7456965446472168,
+        0.4299568831920624,
+        0.43978309631347656,
+        0.4109635651111603,
+        0.3318731486797333,
+        1.0517019033432007,
+        0.9455519914627075,
+        0.7481663227081299,
+        0.32819226384162903,
+        0.5233535766601562,
+        0.3876449465751648
+      ],
+      "max": [
+        0.02667570859193802,
+        3.778841733932495,
+        4.568304061889648,
+        1.0729998350143433,
+        1.2834042310714722,
+        1.6689540147781372,
+        1.0,
+        0.8685938715934753,
+        2.6522276401519775,
+        3.264349937438965,
+        1.1015950441360474,
+        1.1631274223327637,
+        3.160046100616455,
+        1.0
+      ],
+      "min": [
+        -0.858056902885437,
+        0.0,
+        -3.064708653255366e-05,
+        -1.8265033960342407,
+        -0.028474153950810432,
+        -0.8472005128860474,
+        0.0,
+        -2.803896188735962,
+        -0.4660651385784149,
+        -2.5819708753260784e-05,
+        -1.9893873929977417,
+        -1.1286083459854126,
+        -1.736908197402954,
+        0.0
+      ],
+      "q01": [
+        -0.6967605352401733,
+        0.0,
+        -3.064708653255366e-05,
+        -1.720620036125183,
+        -0.014083670675754547,
+        -0.5925237935781479,
+        0.0,
+        -0.04488968480378389,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7743152236938478,
+        -1.1208465099334717,
+        -1.7055805158615112,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.777959144115448,
+        2.902973175048828,
+        1.0099876379966732,
+        1.272336483001709,
+        1.5119220542907714,
+        1.0,
+        0.8364987969398499,
+        2.5737154483795166,
+        2.664341926574707,
+        1.0096371030807485,
+        0.11334303751587624,
+        0.8461964726448059,
+        1.0
+      ]
+    },
+    "num_transitions": 12394,
+    "num_trajectories": 50
+  },
+  "place_phone_stand_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.24955666065216064,
+        0.96904057264328,
+        0.6381116509437561,
+        -0.24100831151008606,
+        -0.11241041123867035,
+        -0.17180098593235016,
+        0.7342271208763123,
+        0.18775422871112823,
+        0.7277160286903381,
+        0.4949023425579071,
+        -0.1834067404270172,
+        0.0859995111823082,
+        0.13992515206336975,
+        0.8067823052406311
+      ],
+      "std": [
+        0.27407583594322205,
+        0.9312117695808411,
+        0.6364781856536865,
+        0.3659789264202118,
+        0.20980769395828247,
+        0.30180302262306213,
+        0.41827327013015747,
+        0.25742045044898987,
+        0.9351555705070496,
+        0.653581440448761,
+        0.33380553126335144,
+        0.19428309798240662,
+        0.28798776865005493,
+        0.3758675456047058
+      ],
+      "max": [
+        0.0,
+        2.447045087814331,
+        2.13200044631958,
+        0.27845299243927,
+        0.08664697408676147,
+        0.3559088408946991,
+        1.0,
+        0.881425678730011,
+        2.4448471069335938,
+        2.0905659198760986,
+        0.17398181557655334,
+        0.8084141612052917,
+        1.3334383964538574,
+        1.0
+      ],
+      "min": [
+        -0.9803099632263184,
+        0.0,
+        0.0,
+        -1.340118169784546,
+        -0.8613136410713196,
+        -1.3788976669311523,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2530916929244995,
+        -0.021880000829696655,
+        -0.4965520203113556,
+        0.0
+      ],
+      "q01": [
+        -0.8396719813346862,
+        0.0,
+        0.0,
+        -1.0591385531425477,
+        -0.7201460123062133,
+        -1.0765761065483093,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0728944265842437,
+        -0.015176629358902573,
+        -0.27935048937797546,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.360727548599243,
+        1.9245160818099976,
+        0.2115602046251297,
+        0.005798054859042168,
+        0.3227096199989319,
+        1.0,
+        0.7152422976493827,
+        2.348403215408325,
+        1.9352505207061768,
+        0.14278633892536163,
+        0.6668316721916199,
+        1.122056484222412,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.2470388114452362,
+        0.959843635559082,
+        0.6315163373947144,
+        -0.2413448542356491,
+        -0.11027531325817108,
+        -0.17068681120872498,
+        0.7342271208763123,
+        0.18596167862415314,
+        0.7207176685333252,
+        0.48970282077789307,
+        -0.18350425362586975,
+        0.08433938771486282,
+        0.13906404376029968,
+        0.8067823052406311
+      ],
+      "std": [
+        0.2737008035182953,
+        0.9307196736335754,
+        0.635271430015564,
+        0.365650475025177,
+        0.20842938125133514,
+        0.30195745825767517,
+        0.41827327013015747,
+        0.2567901611328125,
+        0.9326195120811462,
+        0.6510723233222961,
+        0.33370161056518555,
+        0.19280125200748444,
+        0.2880008816719055,
+        0.3758675456047058
+      ],
+      "max": [
+        0.0,
+        2.447045087814331,
+        2.13200044631958,
+        0.27845299243927,
+        0.08664697408676147,
+        0.3559088408946991,
+        1.0,
+        0.881425678730011,
+        2.4448471069335938,
+        2.0905659198760986,
+        0.17398181557655334,
+        0.8084141612052917,
+        1.3334383964538574,
+        1.0
+      ],
+      "min": [
+        -0.9803099632263184,
+        0.0,
+        0.0,
+        -1.340118169784546,
+        -0.8613136410713196,
+        -1.3788976669311523,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2530916929244995,
+        -0.021880000829696655,
+        -0.4965520203113556,
+        0.0
+      ],
+      "q01": [
+        -0.8396719813346862,
+        0.0,
+        0.0,
+        -1.0591385531425477,
+        -0.7201460123062133,
+        -1.0765761065483093,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0728944265842437,
+        -0.015176629358902573,
+        -0.27935048937797546,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.360727548599243,
+        1.9245160818099976,
+        0.2115602046251297,
+        0.005798054859042168,
+        0.3227096199989319,
+        1.0,
+        0.7152422976493827,
+        2.348403215408325,
+        1.929482665061947,
+        0.14278633892536163,
+        0.6668316721916199,
+        1.122056484222412,
+        1.0
+      ]
+    },
+    "num_transitions": 6340,
+    "num_trajectories": 50
+  },
+  "put_bottles_dustbin_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.10476312041282654,
+        1.279449462890625,
+        0.5679159164428711,
+        0.5262239575386047,
+        0.22428759932518005,
+        0.043430764228105545,
+        0.6643036603927612,
+        0.19642633199691772,
+        1.117813229560852,
+        0.7129987478256226,
+        0.34945419430732727,
+        -0.2572241723537445,
+        -0.014616748318076134,
+        0.6229138970375061
+      ],
+      "std": [
+        0.25748223066329956,
+        0.9038821458816528,
+        0.44731849431991577,
+        0.5311910510063171,
+        0.24035809934139252,
+        0.05469735711812973,
+        0.44946926832199097,
+        0.2710811495780945,
+        0.9252318739891052,
+        0.6049585342407227,
+        0.31488096714019775,
+        0.2908095717430115,
+        0.04302773252129555,
+        0.4715794026851654
+      ],
+      "max": [
+        0.37749066948890686,
+        2.792454242706299,
+        1.926819086074829,
+        1.7764710187911987,
+        1.1897499561309814,
+        0.26964041590690613,
+        1.0,
+        0.5676500201225281,
+        2.6290316581726074,
+        2.1055216789245605,
+        1.4273184537887573,
+        2.08674136956688e-05,
+        0.08746904134750366,
+        1.0
+      ],
+      "min": [
+        -0.5788599848747253,
+        -0.000386573257856071,
+        -0.005483707413077354,
+        -0.016073711216449738,
+        -0.0011532115750014782,
+        -0.05382127687335014,
+        0.0,
+        -0.5238198041915894,
+        -1.2773498383467086e-05,
+        -3.102514165220782e-05,
+        -1.7486241631559096e-05,
+        -1.2793614864349365,
+        -0.28817570209503174,
+        0.0
+      ],
+      "q01": [
+        -0.5524951219558716,
+        -4.5852862058382016e-06,
+        -3.064708653255366e-05,
+        -1.1295316653558984e-05,
+        -6.482168373622699e-06,
+        -0.017715569883584976,
+        0.0,
+        -0.3273748874664307,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7486239812569693e-05,
+        -1.061243543624878,
+        -0.16889580547809602,
+        0.0
+      ],
+      "q99": [
+        0.18636572360992432,
+        2.6412384510040283,
+        1.7621351480484009,
+        1.5735922956466664,
+        0.9350311040878294,
+        0.17157290935516348,
+        1.0,
+        0.5604766869544983,
+        2.5446243667602535,
+        1.8965028524398804,
+        1.0255632400512695,
+        0.0,
+        0.07489971071481705,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.10503867268562317,
+        1.2768235206604004,
+        0.566667914390564,
+        0.5260285139083862,
+        0.2240736186504364,
+        0.043224893510341644,
+        0.6643036603927612,
+        0.19641055166721344,
+        1.117813229560852,
+        0.7129987478256226,
+        0.34945419430732727,
+        -0.2572241723537445,
+        -0.014616748318076134,
+        0.6229138970375061
+      ],
+      "std": [
+        0.257255882024765,
+        0.9052372574806213,
+        0.44780004024505615,
+        0.5313783884048462,
+        0.2404986470937729,
+        0.05462057143449783,
+        0.4494692385196686,
+        0.27109846472740173,
+        0.9252316355705261,
+        0.6049585342407227,
+        0.31488096714019775,
+        0.2908095717430115,
+        0.04302773252129555,
+        0.4715794026851654
+      ],
+      "max": [
+        0.37749066948890686,
+        2.792454242706299,
+        1.926819086074829,
+        1.7764710187911987,
+        1.1897499561309814,
+        0.26964041590690613,
+        1.0,
+        0.5676500201225281,
+        2.6290316581726074,
+        2.1055216789245605,
+        1.4273184537887573,
+        2.08674136956688e-05,
+        0.08746904134750366,
+        1.0
+      ],
+      "min": [
+        -0.5788599848747253,
+        -0.000386573257856071,
+        -0.005483707413077354,
+        -0.016073711216449738,
+        -0.0011532115750014782,
+        -0.05382127687335014,
+        0.0,
+        -0.5238198041915894,
+        -1.2773498383467086e-05,
+        -3.102514165220782e-05,
+        -1.7486241631559096e-05,
+        -1.2793614864349365,
+        -0.28817570209503174,
+        0.0
+      ],
+      "q01": [
+        -0.5524951219558716,
+        -4.5852862058382016e-06,
+        -3.064708653255366e-05,
+        -1.1295316653558984e-05,
+        -6.482168373622699e-06,
+        -0.017715569883584976,
+        0.0,
+        -0.3273748874664307,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7486239812569693e-05,
+        -1.061243543624878,
+        -0.16889580547809602,
+        0.0
+      ],
+      "q99": [
+        0.18636572360992432,
+        2.6412384510040283,
+        1.7621351480484009,
+        1.5735922956466664,
+        0.9350311040878294,
+        0.17157290935516348,
+        1.0,
+        0.5604766869544983,
+        2.5446243667602535,
+        1.8965028524398804,
+        1.0255632400512695,
+        0.0,
+        0.07489971071481705,
+        1.0
+      ]
+    },
+    "num_transitions": 31457,
+    "num_trajectories": 50
+  },
+  "put_object_cabinet_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.28438690304756165,
+        1.764165997505188,
+        1.1697032451629639,
+        -0.8346169590950012,
+        -0.027913035824894905,
+        0.2120014876127243,
+        0.2626643180847168,
+        0.7117581367492676,
+        1.5304840803146362,
+        0.6800471544265747,
+        0.7674376368522644,
+        0.6864986419677734,
+        -0.9722089767456055,
+        0.4657360017299652
+      ],
+      "std": [
+        0.24526149034500122,
+        0.512263298034668,
+        0.6820644736289978,
+        0.5534589886665344,
+        0.1659320443868637,
+        0.859703004360199,
+        0.421505331993103,
+        0.5065756440162659,
+        0.9997316002845764,
+        0.4915132224559784,
+        0.6616247296333313,
+        0.5067867636680603,
+        0.763378381729126,
+        0.490205854177475
+      ],
+      "max": [
+        0.08587173372507095,
+        2.7109200954437256,
+        3.1437699794769287,
+        1.352994680404663,
+        0.03168417513370514,
+        1.9688830375671387,
+        1.0,
+        1.2608709335327148,
+        2.8519444465637207,
+        3.266448497772217,
+        1.3512014150619507,
+        1.2738021612167358,
+        0.7937034964561462,
+        1.0
+      ],
+      "min": [
+        -1.1788203716278076,
+        0.0,
+        0.0,
+        -1.865718960762024,
+        -1.1659986972808838,
+        -1.1686975955963135,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.9566882848739624,
+        -0.005439999978989363,
+        -1.559630036354065,
+        0.0
+      ],
+      "q01": [
+        -1.1621215343475342,
+        0.0,
+        0.0,
+        -1.8261551237106324,
+        -1.1501946449279785,
+        -1.1634292602539062,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3072348833084106,
+        -0.004720241297036409,
+        -1.554312481880188,
+        0.0
+      ],
+      "q99": [
+        0.07353582233190536,
+        2.61279296875,
+        2.9014140892028806,
+        1.318111538887024,
+        0.02163825549185276,
+        1.9688830375671387,
+        1.0,
+        1.2599315643310547,
+        2.408996105194092,
+        1.3551753759384155,
+        1.3495060205459595,
+        1.2695887088775635,
+        0.7937034964561462,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.2818736433982849,
+        1.7552909851074219,
+        1.161139726638794,
+        -0.8293845057487488,
+        -0.02772539108991623,
+        0.21406246721744537,
+        0.26281240582466125,
+        0.707289457321167,
+        1.5223047733306885,
+        0.6766162514686584,
+        0.7630574703216553,
+        0.6822458505630493,
+        -0.9668338298797607,
+        0.46929028630256653
+      ],
+      "std": [
+        0.24459987878799438,
+        0.5218791961669922,
+        0.6819084286689758,
+        0.5535831451416016,
+        0.16536164283752441,
+        0.8581108450889587,
+        0.42160049080848694,
+        0.5074564814567566,
+        1.0031490325927734,
+        0.4923613667488098,
+        0.6617483496665955,
+        0.5075437426567078,
+        0.7646470665931702,
+        0.49047353863716125
+      ],
+      "max": [
+        0.08587173372507095,
+        2.7109200954437256,
+        3.1437699794769287,
+        1.352994680404663,
+        0.03168417513370514,
+        1.9688830375671387,
+        1.0,
+        1.2608709335327148,
+        2.8519444465637207,
+        3.266448497772217,
+        1.3512014150619507,
+        1.2738021612167358,
+        0.7937034964561462,
+        1.0
+      ],
+      "min": [
+        -1.1788203716278076,
+        0.0,
+        0.0,
+        -1.865718960762024,
+        -1.1659986972808838,
+        -1.1686975955963135,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.9566882848739624,
+        -0.005439999978989363,
+        -1.559630036354065,
+        0.0
+      ],
+      "q01": [
+        -1.1621215343475342,
+        0.0,
+        0.0,
+        -1.8261551237106324,
+        -1.1501946449279785,
+        -1.1634292602539062,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3071338891983033,
+        -0.004720241297036409,
+        -1.554312481880188,
+        0.0
+      ],
+      "q99": [
+        0.07353582233190536,
+        2.61279296875,
+        2.9014140892028806,
+        1.318111538887024,
+        0.021636107936501503,
+        1.9688830375671387,
+        1.0,
+        1.2599315643310547,
+        2.408996105194092,
+        1.3551753759384155,
+        1.3495060205459595,
+        1.2695887088775635,
+        0.7937034964561462,
+        1.0
+      ]
+    },
+    "num_transitions": 13505,
+    "num_trajectories": 50
+  },
+  "stack_blocks_two_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.21114705502986908,
+        0.9412369132041931,
+        0.7314395904541016,
+        -0.4761776030063629,
+        -0.0009380271076224744,
+        -0.07520589232444763,
+        0.7869701385498047,
+        0.26069143414497375,
+        1.1080588102340698,
+        0.8630656003952026,
+        -0.559351921081543,
+        0.007944311946630478,
+        0.08012033998966217,
+        0.7491208910942078
+      ],
+      "std": [
+        0.3050781488418579,
+        1.0409293174743652,
+        0.8131765127182007,
+        0.5270503759384155,
+        0.008852960541844368,
+        0.4395352900028229,
+        0.3933006227016449,
+        0.3120238780975342,
+        1.047856092453003,
+        0.8200691938400269,
+        0.5292724967002869,
+        0.023433445021510124,
+        0.47746512293815613,
+        0.41560548543930054
+      ],
+      "max": [
+        0.020010676234960556,
+        2.5165865421295166,
+        2.384333372116089,
+        2.1778516384074464e-05,
+        0.10835044085979462,
+        1.2322380542755127,
+        1.0,
+        0.8241801261901855,
+        2.444321393966675,
+        2.2086756229400635,
+        0.0,
+        0.18467359244823456,
+        0.8262989521026611,
+        1.0
+      ],
+      "min": [
+        -0.8325228095054626,
+        -4.1955649976443965e-06,
+        -3.064708653255366e-05,
+        -1.4605095386505127,
+        -0.0383315235376358,
+        -0.8282271027565002,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.7174886781722307e-05,
+        -1.3987765312194824,
+        -0.010218792594969273,
+        -1.1888049840927124,
+        0.0
+      ],
+      "q01": [
+        -0.8252553939819336,
+        0.0,
+        -3.064708653255366e-05,
+        -1.3362677097320557,
+        -0.026790249347686767,
+        -0.8190370202064514,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.340792179107666,
+        -0.006735008861869574,
+        -1.0526111125946045,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.4263905525207523,
+        2.078766918182373,
+        2.1778516384074464e-05,
+        0.019878851249814044,
+        1.0974429607391358,
+        1.0,
+        0.8126579523086548,
+        2.4025102615356446,
+        2.0527486324310305,
+        0.0,
+        0.15595661997795107,
+        0.8210508823394775,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.20990003645420074,
+        0.9378898739814758,
+        0.7284502387046814,
+        -0.47404396533966064,
+        -0.0009204319212585688,
+        -0.07391548156738281,
+        0.7869701385498047,
+        0.25938308238983154,
+        1.1046009063720703,
+        0.8598967790603638,
+        -0.557152271270752,
+        0.007918022572994232,
+        0.07882484793663025,
+        0.7491208910942078
+      ],
+      "std": [
+        0.3042908310890198,
+        1.0406111478805542,
+        0.8124014139175415,
+        0.526269793510437,
+        0.008842087350785732,
+        0.4385771155357361,
+        0.3933006227016449,
+        0.3114229738712311,
+        1.047912836074829,
+        0.8195027112960815,
+        0.5286997556686401,
+        0.023432398214936256,
+        0.47657015919685364,
+        0.41560545563697815
+      ],
+      "max": [
+        0.020010676234960556,
+        2.5165865421295166,
+        2.384333372116089,
+        2.1778516384074464e-05,
+        0.10835044085979462,
+        1.2322380542755127,
+        1.0,
+        0.8241801261901855,
+        2.444321393966675,
+        2.2086756229400635,
+        0.0,
+        0.18467359244823456,
+        0.8262989521026611,
+        1.0
+      ],
+      "min": [
+        -0.8325228095054626,
+        -4.1955649976443965e-06,
+        -3.064708653255366e-05,
+        -1.4605095386505127,
+        -0.0383315235376358,
+        -0.8282271027565002,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.7174886781722307e-05,
+        -1.3987765312194824,
+        -0.010218792594969273,
+        -1.1888049840927124,
+        0.0
+      ],
+      "q01": [
+        -0.8252553939819336,
+        0.0,
+        -3.064708653255366e-05,
+        -1.333071780204773,
+        -0.026790249347686767,
+        -0.8190370202064514,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.3284639596939087,
+        -0.006735008861869574,
+        -1.0526111125946045,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.4263905525207523,
+        2.078766918182373,
+        2.1778516384074464e-05,
+        0.019878851249814044,
+        1.0974429607391358,
+        1.0,
+        0.812503182888031,
+        2.4025102615356446,
+        2.0527486324310305,
+        0.0,
+        0.15595661997795107,
+        0.8210508823394775,
+        1.0
+      ]
+    },
+    "num_transitions": 15641,
+    "num_trajectories": 50
+  },
+  "stack_bowls_two_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.1218431144952774,
+        0.879644513130188,
+        0.6815189719200134,
+        -0.45526257157325745,
+        0.0535038486123085,
+        -0.09123744815587997,
+        0.7937045693397522,
+        0.1586911976337433,
+        1.0623502731323242,
+        0.8378229737281799,
+        -0.5510385036468506,
+        -0.0523577518761158,
+        0.17441710829734802,
+        0.7524454593658447
+      ],
+      "std": [
+        0.20389510691165924,
+        0.9881824851036072,
+        0.7890526652336121,
+        0.5182338953018188,
+        0.10292115807533264,
+        0.15442776679992676,
+        0.3886118233203888,
+        0.2241450399160385,
+        1.0105440616607666,
+        0.8408956527709961,
+        0.5377111434936523,
+        0.10513534396886826,
+        0.2661285400390625,
+        0.41391754150390625
+      ],
+      "max": [
+        0.10931042581796646,
+        2.699620485305786,
+        2.8983492851257324,
+        2.1778516384074464e-05,
+        0.3715807795524597,
+        0.09972000122070312,
+        1.0,
+        0.6297850608825684,
+        3.2168734073638916,
+        3.589359998703003,
+        0.0,
+        0.12872982025146484,
+        1.9198805093765259,
+        1.0
+      ],
+      "min": [
+        -0.583745002746582,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7004367113113403,
+        -0.019333183765411377,
+        -1.0937238931655884,
+        0.0,
+        -0.14522859454154968,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.9368948936462402,
+        -0.35954946279525757,
+        -0.08796866983175278,
+        0.0
+      ],
+      "q01": [
+        -0.5723252892494202,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4582831704616546,
+        -0.0065253665670752525,
+        -0.41102612018585205,
+        0.0,
+        -0.07947887197136878,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.6614161729812622,
+        -0.32789366245269774,
+        -0.06472839415073395,
+        0.0
+      ],
+      "q99": [
+        0.08905640229582787,
+        2.5206500244140626,
+        2.4590754604339606,
+        2.1778516384074464e-05,
+        0.3437314799427987,
+        0.09290612868964686,
+        1.0,
+        0.6060269474983215,
+        2.6554339265823366,
+        2.820633199214936,
+        0.0,
+        0.05726956911385072,
+        0.6783580780029297,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.12101656943559647,
+        0.8765714764595032,
+        0.6787510514259338,
+        -0.453287273645401,
+        0.05318109318614006,
+        -0.09065449237823486,
+        0.7937045693397522,
+        0.15774448215961456,
+        1.0592150688171387,
+        0.8349401950836182,
+        -0.5490079522132874,
+        -0.05208369344472885,
+        0.17349445819854736,
+        0.7524454593658447
+      ],
+      "std": [
+        0.20327353477478027,
+        0.9879177212715149,
+        0.7883735299110413,
+        0.5175690650939941,
+        0.10275426506996155,
+        0.15405894815921783,
+        0.3886118233203888,
+        0.2235718071460724,
+        1.0107218027114868,
+        0.8406001329421997,
+        0.5373188853263855,
+        0.10504182428121567,
+        0.26566997170448303,
+        0.41391754150390625
+      ],
+      "max": [
+        0.10931042581796646,
+        2.699620485305786,
+        2.8983492851257324,
+        2.1778516384074464e-05,
+        0.3715807795524597,
+        0.09972000122070312,
+        1.0,
+        0.6297850608825684,
+        3.2168734073638916,
+        3.589359998703003,
+        0.0,
+        0.12872982025146484,
+        1.9198805093765259,
+        1.0
+      ],
+      "min": [
+        -0.583745002746582,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7004367113113403,
+        -0.019333183765411377,
+        -1.0937238931655884,
+        0.0,
+        -0.14522859454154968,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.9368948936462402,
+        -0.35954946279525757,
+        -0.08796866983175278,
+        0.0
+      ],
+      "q01": [
+        -0.5723252892494202,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4582276344299316,
+        -0.0065253665670752525,
+        -0.41102612018585205,
+        0.0,
+        -0.07947887197136878,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.6614161729812622,
+        -0.32789366245269774,
+        -0.06472839415073395,
+        0.0
+      ],
+      "q99": [
+        0.08905640229582787,
+        2.5206500244140626,
+        2.4590754604339606,
+        2.1778516384074464e-05,
+        0.3437314799427987,
+        0.09290612868964686,
+        1.0,
+        0.6060269474983215,
+        2.6554339265823366,
+        2.820633199214936,
+        0.0,
+        0.05726956911385072,
+        0.6783580780029297,
+        1.0
+      ]
+    },
+    "num_transitions": 15948,
+    "num_trajectories": 50
+  }
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--10000_chkpt/lora_adapter/adapter_config.json

@@ -0,0 +1,45 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "OpenVLAForActionPrediction",
+    "parent_library": "transformers_modules.openvla-7b.modeling_prismatic"
+  },
+  "base_model_name_or_path": "/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/ai_models/openvla/openvla-7b",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q",
+    "fc1",
+    "lm_head",
+    "fc3",
+    "qkv",
+    "gate_proj",
+    "o_proj",
+    "up_proj",
+    "k_proj",
+    "v_proj",
+    "down_proj",
+    "proj",
+    "q_proj",
+    "kv",
+    "fc2"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--10000_chkpt/processor_config.json

@@ -0,0 +1,6 @@
+{
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "processor_class": "PrismaticProcessor"
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--20000_chkpt/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<PAD>": 32000
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--20000_chkpt/lora_adapter/README.md

@@ -0,0 +1,202 @@
+---
+base_model: /inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/ai_models/openvla/openvla-7b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--20000_chkpt/processing_prismatic.py

@@ -0,0 +1,257 @@
+"""
+processing_prismatic.py
+
+HuggingFace-style preprocessor definitions for Prismatic VLMs, inheriting from `ProcessorMixin`. Default configuration
+specifies `siglip-224px+7b`.
+"""
+
+from typing import Any, ClassVar, List, Optional, Tuple, Union
+
+import timm.data
+import torch
+import torchvision.transforms.functional as TVF
+from PIL import Image
+from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
+from transformers import PreTrainedTokenizerBase
+from transformers.image_processing_utils import BatchFeature, ImageProcessingMixin
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+# === Image Processing ===
+def letterbox_pad_transform(image: Image.Image, padding_fill_value: Tuple[int, int, int]) -> Image.Image:
+    """Given a PIL.Image, pad to square by adding a symmetric border around the height/width."""
+    (w, h), max_wh = image.size, max(image.size)
+    horizontal_pad, vertical_pad = int((max_wh - w) / 2), int((max_wh - h) / 2)
+    padding = (horizontal_pad, vertical_pad, horizontal_pad, vertical_pad)
+
+    return TVF.pad(image, padding, fill=padding_fill_value, padding_mode="constant")
+
+
+class PrismaticImageProcessor(ImageProcessingMixin):
+    model_input_names: ClassVar[List[str]] = ["pixel_values"]
+
+    def __init__(
+        self,
+        use_fused_vision_backbone: bool = False,
+        image_resize_strategy: str = "letterbox",
+        input_sizes: Optional[List[Tuple[int, int, int]]] = None,
+        interpolations: Optional[List[str]] = None,
+        means: Optional[List[Tuple[float, float, float]]] = None,
+        stds: Optional[List[Tuple[float, float, float]]] = None,
+        **kwargs: str,
+    ) -> None:
+        """
+        Initialize a PrismaticImageProcessor as a wrapper around a torchvision transform; this transform will be
+        created by TIMM, and edited to follow our custom `image_resize_strategy` logic.
+
+        @param use_fused_vision_backbone: Boolean indicating single or fused (dual) vision backbone
+        @param image_resize_strategy: Prismatic image resize strategy in < resize-naive | resize-crop | letterbox >
+        @param input_size: [TIMM :: `data_cfg`] Input image size as tuple (channels, width, height)
+        @param interpolation: [TIMM :: `data_cfg`] Interpolation as string (default: "bicubic")
+        @param mean: [TIMM :: `data_cfg`] Normalization mean as float tuple (or two-tuple if `fused_backbone`)
+        @param std: [TIMM :: `data_cfg`] Normalization std as float tuple (or two-tuple if `fused_backbone`)
+        """
+        self.use_fused_vision_backbone = use_fused_vision_backbone
+        self.image_resize_strategy = image_resize_strategy
+
+        # Handle `None` default values
+        input_sizes = [(3, 224, 224)] if input_sizes is None else input_sizes
+        means = [(0.5, 0.5, 0.5)] if means is None else means
+        stds = [(0.5, 0.5, 0.5)] if stds is None else stds
+
+        # TIMM `data_cfg` Parameters
+        self.input_sizes, self.interpolations, self.means, self.stds = input_sizes, interpolations, means, stds
+
+        # Grab torchvision transforms via TIMM =>> need to parse for specific "functional" transform values!
+        self.tvf_resize_params, self.tvf_crop_params, self.tvf_normalize_params = [], [], []
+        self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+        for idx in range(len(input_sizes)):
+            transform = timm.data.create_transform(
+                input_size=self.input_sizes[idx],
+                interpolation=self.interpolations[idx],
+                mean=self.means[idx],
+                std=self.stds[idx],
+                crop_pct=1.0,  # Set to 1.0 to ignore cropping (initial Resize sets `input_size`)
+                crop_mode="center",  # Default crop mode -- no-op when `crop_pct == 1.0`
+                is_training=False,  # No image augmentations when loading the transform!
+            )
+
+            # [Validation] Ensure appropriate transform structure, expected sizes
+            if not (
+                isinstance(transform, Compose)
+                and (len(transform.transforms) == 4)
+                and isinstance(transform.transforms[0], Resize)
+                and isinstance(transform.transforms[1], CenterCrop)
+                and isinstance(transform.transforms[2], ToTensor)
+                and isinstance(transform.transforms[3], Normalize)
+                and (transform.transforms[0].size == self.input_sizes[idx][-1])
+                and (transform.transforms[1].size == self.input_sizes[idx][-2:])
+            ):
+                raise ValueError(f"Unexpected TIMM image transformation structure/sizes: `{transform}`")
+
+            # HF Image Processors *must* be JSON-serializable; as such, cannot have torchvision. as an attribute.
+            #   => Instead, we're going to parse the transform and call "torchvision.transforms.functional" (`tvf`)
+            resize_t, crop_t, norm_t = transform.transforms[0], transform.transforms[1], transform.transforms[3]
+            self.tvf_resize_params.append(
+                {
+                    "size": resize_t.size,
+                    "interpolation": TVF.pil_modes_mapping[resize_t.interpolation],
+                    "max_size": None,
+                    "antialias": True,
+                }
+            )
+            self.tvf_crop_params.append({"output_size": crop_t.size})
+            self.tvf_normalize_params.append(
+                {
+                    "mean": norm_t.mean.float().numpy().tolist(),
+                    "std": norm_t.std.float().numpy().tolist(),
+                    "inplace": False,
+                }
+            )
+            self.tvf_do_letterbox, self.tvf_letterbox_fill = False, None
+
+            # Handle Prismatic `image_resize_strategy`
+            if self.image_resize_strategy == "resize-naive":
+                self.tvf_resize_params[idx]["size"] = (resize_t.size, resize_t.size)
+            elif self.image_resize_strategy == "letterbox":
+                self.tvf_do_letterbox, self.tvf_letterbox_fill = True, tuple([int(x * 255) for x in self.means[idx]])
+            elif self.image_resize_strategy == "resize-crop":
+                pass
+            else:
+                raise ValueError(f"Image resize strategy `{self.image_resize_strategy}` is not supported!")
+
+        # Dispatch **kwargs to super()
+        super().__init__(**kwargs)
+
+    def apply_transform(self, img: Image.Image) -> torch.Tensor:
+        """Apply `functional` variant of TIMM's Transform = Compose([Resize -> CenterCrop -> ToTensor -> Normalize])"""
+        if self.tvf_do_letterbox:
+            img = letterbox_pad_transform(img, self.tvf_letterbox_fill)
+
+        # [Contract] Fused Backbones expect "channel-stacked" inputs; we'll unpack on the model side!
+        imgs_t = []
+        for idx in range(len(self.input_sizes)):
+            img_idx = TVF.resize(img, **self.tvf_resize_params[idx])
+            img_idx = TVF.center_crop(img_idx, **self.tvf_crop_params[idx])
+            img_idx_t = TVF.to_tensor(img_idx)
+            img_idx_t = TVF.normalize(img_idx_t, **self.tvf_normalize_params[idx])
+            imgs_t.append(img_idx_t)
+
+        # [Contract] `imgs_t` is a list of Tensors of shape [3, input_size, input_size]; stack along dim = 0
+        img_t = torch.vstack(imgs_t)
+
+        return img_t
+
+    def preprocess(
+        self,
+        images: Union[Image.Image, List[Image.Image]],
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **_: str,
+    ) -> BatchFeature:
+        """
+        Preprocess an image (or batch of images); note that unlike the `transformers :: BaseImageProcessor` we
+        explicitly only handle PIL.Image.Image instances for simplicity.
+
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param return_tensors: BatchFeature default Tensor format (e.g., "pt" for torch); if None, returns np.ndarray
+
+        @return: Instance of `transformers :: BatchFeature` with a single key "pixel_values"
+        """
+        if not isinstance(images, list):
+            images = [images]
+
+        # Apply `self.img_transform` to each image (will return list of torch.Tensors); stack into "batched" Tensor
+        pixel_values = torch.stack([self.apply_transform(img.convert("RGB")) for img in images])
+
+        # Return BatchFeature =>> note that for compatibility, constructor expects Dict[str, np.ndarray], so we convert
+        return BatchFeature(data={"pixel_values": pixel_values.float().numpy()}, tensor_type=return_tensors)
+
+    def __call__(self, images: Union[Image.Image, List[Image.Image]], **kwargs) -> BatchFeature:
+        return self.preprocess(images, **kwargs)
+
+
+# === PrismaticProcessor =>> Wraps both ImageProcessor and Tokenizer ===
+#   =>> https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava/processing_llava.py
+class PrismaticProcessor(ProcessorMixin):
+    attributes: ClassVar[List[str]] = ["image_processor", "tokenizer"]
+    image_processor_class: str = "AutoImageProcessor"
+    tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: Optional[ImageProcessingMixin] = None,
+        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+    ) -> None:
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        images: Union[Image.Image, List[Image.Image]],
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Optional[Union[bool, str, TruncationStrategy]] = None,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Preprocess a given (batch) of text/images for a Prismatic VLM; forwards text to the underlying LLM's tokenizer,
+        forwards images to PrismaticImageProcessor.
+
+        @param text: The (batch) of text to encode; must be a string or list of strings.
+        @param images: A (batch of) PIL.Image.Image instance(s) to preprocess.
+        @param padding: Sequence padding strategy (if multiple specified) in < True = "longest" | "max_length" | False >
+        @param truncation: Truncation strategy for the output sequences; requires `max_length` to be specified
+        @param max_length: Maximum length (in tokens) to truncate
+        @param return_tensors: Type of return tensors (usually "pt" or TensorType.PYTORCH)
+
+        @return: BatchFeature with keys for `input_ids`, `attention_mask` and `pixel_values`.
+        """
+        pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
+        text_inputs = self.tokenizer(
+            text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
+        )
+
+        # [Validate] Need same number of images and text inputs!
+        if pixel_values.shape[0] != text_inputs.input_ids.shape[0]:
+            raise ValueError("Batch is malformed; expected same number of images and text inputs!")
+
+        return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
+
+    # === Tokenizer Dispatch Utilities =>> check `PreTrainedTokenizerBase` for documentation ===
+    def batch_decode(
+        self,
+        sequences: Union[List[int], List[List[int]], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> List[str]:
+        return self.tokenizer.batch_decode(
+            sequences=sequences,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    def decode(
+        self,
+        token_ids: Union[int, List[int], torch.Tensor, Any],  # `Any` = np.ndarray | tf.Tensor
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: Optional[bool] = None,
+        **kwargs: str,
+    ) -> str:
+        return self.tokenizer.decode(
+            token_ids=token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self) -> List[str]:
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--20000_chkpt/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<PAD>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000--20000_chkpt/tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<PAD>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoProcessor": "processing_prismatic.PrismaticProcessor"
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<PAD>",
+  "padding_side": "right",
+  "processor_class": "PrismaticProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

results/simvla_patch_all_25/openvla-7b+aloha_agilex_robotwin2_benchmark+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_patch_all_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M20000-F10000-D10000/dataset_statistics.json

@@ -0,0 +1,2810 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  },
+  "handover_mic_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.3111798167228699,
+        0.9980528354644775,
+        0.5811634659767151,
+        0.23248183727264404,
+        0.1577642261981964,
+        -0.023892972618341446,
+        0.607450008392334,
+        0.37389707565307617,
+        1.1807876825332642,
+        0.6784368753433228,
+        0.25683099031448364,
+        -0.1075415387749672,
+        0.08219857513904572,
+        0.5450003743171692
+      ],
+      "std": [
+        0.2626854181289673,
+        0.7778211832046509,
+        0.46585971117019653,
+        0.5015608668327332,
+        0.2322310358285904,
+        0.10976110398769379,
+        0.4736306667327881,
+        0.2574075758457184,
+        0.7576491832733154,
+        0.4595378041267395,
+        0.5935487747192383,
+        0.16007103025913239,
+        0.14593657851219177,
+        0.4819530248641968
+      ],
+      "max": [
+        0.0,
+        2.1762497425079346,
+        1.5628178119659424,
+        0.990909993648529,
+        0.8559104800224304,
+        0.11719957739114761,
+        1.0,
+        0.837027370929718,
+        2.206538200378418,
+        1.6232832670211792,
+        1.1574815511703491,
+        0.2886713147163391,
+        0.5811867117881775,
+        1.0
+      ],
+      "min": [
+        -0.8862171173095703,
+        0.0,
+        0.0,
+        -1.0514172315597534,
+        -0.39507749676704407,
+        -0.4001063406467438,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0771245956420898,
+        -0.8672545552253723,
+        -0.07416637241840363,
+        0.0
+      ],
+      "q01": [
+        -0.7671213400363922,
+        0.0,
+        0.0,
+        -0.9784595704078675,
+        -0.27155110239982605,
+        -0.3553581619262695,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0119924187660216,
+        -0.651338517665863,
+        -0.06671902537345886,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.131481647491455,
+        1.404119849205017,
+        0.9854996800422668,
+        0.7671356797218323,
+        0.1121677927672863,
+        1.0,
+        0.7132604718208313,
+        2.139058828353882,
+        1.4547646927833575,
+        1.1053520989418035,
+        0.1410233974456787,
+        0.4914003610610962,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.30907687544822693,
+        0.9918185472488403,
+        0.5777602791786194,
+        0.22962018847465515,
+        0.15603166818618774,
+        -0.02409629337489605,
+        0.610062837600708,
+        0.37142202258110046,
+        1.1745105981826782,
+        0.6752545237541199,
+        0.2535216212272644,
+        -0.10635410249233246,
+        0.08214741945266724,
+        0.546892523765564
+      ],
+      "std": [
+        0.26320481300354004,
+        0.7802274823188782,
+        0.4670981466770172,
+        0.5009189248085022,
+        0.23124119639396667,
+        0.10963220149278641,
+        0.47306305170059204,
+        0.2583112120628357,
+        0.7615904211997986,
+        0.4614890217781067,
+        0.5927368402481079,
+        0.15948225557804108,
+        0.14593760669231415,
+        0.4817669689655304
+      ],
+      "max": [
+        0.0,
+        2.1762497425079346,
+        1.5628178119659424,
+        0.990909993648529,
+        0.8559104800224304,
+        0.11719957739114761,
+        1.0,
+        0.837027370929718,
+        2.206538200378418,
+        1.6232832670211792,
+        1.1574815511703491,
+        0.2886713147163391,
+        0.5811867117881775,
+        1.0
+      ],
+      "min": [
+        -0.8862171173095703,
+        0.0,
+        0.0,
+        -1.0514172315597534,
+        -0.39507749676704407,
+        -0.4001063406467438,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0771245956420898,
+        -0.8672545552253723,
+        -0.07416637241840363,
+        0.0
+      ],
+      "q01": [
+        -0.7671213400363922,
+        0.0,
+        0.0,
+        -0.9784595704078675,
+        -0.27155110239982605,
+        -0.3553581619262695,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0119924187660216,
+        -0.651338517665863,
+        -0.06671902537345886,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.131481647491455,
+        1.404119849205017,
+        0.9854996800422668,
+        0.7671356797218323,
+        0.11196876823902131,
+        1.0,
+        0.7132604718208313,
+        2.139058828353882,
+        1.4547646927833575,
+        1.0914406299591126,
+        0.1410233974456787,
+        0.4914003610610962,
+        1.0
+      ]
+    },
+    "num_transitions": 11099,
+    "num_trajectories": 50
+  },
+  "lift_pot_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.08297546207904816,
+        1.409603238105774,
+        0.9623388648033142,
+        -0.41438642144203186,
+        0.6548429131507874,
+        -0.1665104776620865,
+        0.3988376259803772,
+        0.07074239104986191,
+        1.438857078552246,
+        1.0271384716033936,
+        -0.4811238646507263,
+        -0.6411229968070984,
+        0.13417655229568481,
+        0.3988376259803772
+      ],
+      "std": [
+        0.07541920989751816,
+        0.9829710721969604,
+        0.7201086282730103,
+        0.4319354295730591,
+        0.44529980421066284,
+        0.2749307453632355,
+        0.25010964274406433,
+        0.07464443892240524,
+        1.0042012929916382,
+        0.7712593078613281,
+        0.46114370226860046,
+        0.4362685978412628,
+        0.2527497708797455,
+        0.25010964274406433
+      ],
+      "max": [
+        0.044971514493227005,
+        2.7116494178771973,
+        2.7667176723480225,
+        0.16915999352931976,
+        1.0692775249481201,
+        0.3872540295124054,
+        1.0,
+        0.3003857135772705,
+        2.6776700019836426,
+        2.6789329051971436,
+        0.1484600007534027,
+        0.0,
+        0.8275586366653442,
+        1.0
+      ],
+      "min": [
+        -0.2611783444881439,
+        0.0,
+        0.0,
+        -1.7497934103012085,
+        0.0,
+        -0.7937148809432983,
+        0.0,
+        -0.036408890038728714,
+        0.0,
+        0.0,
+        -1.7768168449401855,
+        -1.0369138717651367,
+        -0.34945493936538696,
+        0.0
+      ],
+      "q01": [
+        -0.24835357069969177,
+        0.0,
+        0.0,
+        -1.4652207350730897,
+        0.0,
+        -0.7823562324047089,
+        0.0,
+        -0.02311648927628994,
+        0.0,
+        0.0,
+        -1.5518183708190918,
+        -1.0312373971939086,
+        -0.27689045667648315,
+        0.0
+      ],
+      "q99": [
+        0.018272288143634796,
+        2.6518030166625977,
+        2.3128650331497194,
+        0.1131945550441742,
+        1.0648717546463013,
+        0.30490371704101565,
+        0.9623115658760071,
+        0.2519652831554413,
+        2.6547678661346437,
+        2.426756353378296,
+        0.004153958521783352,
+        0.0,
+        0.7562372756004334,
+        0.9623115658760071
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.08176180720329285,
+        1.390768051147461,
+        0.9472193121910095,
+        -0.4064285159111023,
+        0.6457239389419556,
+        -0.1636435091495514,
+        0.4078385531902313,
+        0.06947626918554306,
+        1.4194576740264893,
+        1.0109381675720215,
+        -0.47207584977149963,
+        -0.6321477890014648,
+        0.1318783462047577,
+        0.4078385531902313
+      ],
+      "std": [
+        0.07548711448907852,
+        0.9894453287124634,
+        0.7214877009391785,
+        0.4296827018260956,
+        0.44820061326026917,
+        0.2734803557395935,
+        0.253564715385437,
+        0.074403315782547,
+        1.0106186866760254,
+        0.7725314497947693,
+        0.4590045213699341,
+        0.4390813708305359,
+        0.2512795925140381,
+        0.253564715385437
+      ],
+      "max": [
+        0.044971514493227005,
+        2.7116494178771973,
+        2.7667176723480225,
+        0.16915999352931976,
+        1.0692775249481201,
+        0.3872540295124054,
+        1.0,
+        0.30037084221839905,
+        2.6776700019836426,
+        2.6789329051971436,
+        0.1484600007534027,
+        0.0,
+        0.8275586366653442,
+        1.0
+      ],
+      "min": [
+        -0.2611783444881439,
+        0.0,
+        0.0,
+        -1.7497934103012085,
+        0.0,
+        -0.7937148809432983,
+        0.0,
+        -0.036408890038728714,
+        0.0,
+        0.0,
+        -1.7768168449401855,
+        -1.0369138717651367,
+        -0.34945493936538696,
+        0.0
+      ],
+      "q01": [
+        -0.24835357069969177,
+        0.0,
+        0.0,
+        -1.4640827035903932,
+        0.0,
+        -0.7821481037139892,
+        0.0,
+        -0.02311648927628994,
+        0.0,
+        0.0,
+        -1.5469084978103638,
+        -1.0312373971939086,
+        -0.27689045667648315,
+        0.0
+      ],
+      "q99": [
+        0.018272288143634796,
+        2.6518030166625977,
+        2.2994370603561403,
+        0.1131945550441742,
+        1.0648064422607422,
+        0.30490371704101565,
+        1.0,
+        0.2516305303573608,
+        2.654694414138794,
+        2.3750886774063114,
+        0.004153958521783352,
+        0.0,
+        0.756147563457489,
+        1.0
+      ]
+    },
+    "num_transitions": 5555,
+    "num_trajectories": 50
+  },
+  "move_can_pot_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.03227013349533081,
+        0.9706242084503174,
+        0.8449752926826477,
+        -0.4254174828529358,
+        0.05570398271083832,
+        0.09763211756944656,
+        0.7630919218063354,
+        0.043113451451063156,
+        1.036581039428711,
+        0.9449251294136047,
+        -0.5384683012962341,
+        -0.01411368977278471,
+        -0.09070954471826553,
+        0.7476586103439331
+      ],
+      "std": [
+        0.11224070936441422,
+        1.1117055416107178,
+        1.0112414360046387,
+        0.6318230628967285,
+        0.18493546545505524,
+        0.2674776613712311,
+        0.40844425559043884,
+        0.10649054497480392,
+        1.1204936504364014,
+        1.0428167581558228,
+        0.589298665523529,
+        0.058494143187999725,
+        0.27779391407966614,
+        0.41657936573028564
+      ],
+      "max": [
+        0.3149413466453552,
+        3.0223748683929443,
+        3.342350721359253,
+        1.4929821491241455,
+        1.153774380683899,
+        0.9257752299308777,
+        1.0,
+        0.32490795850753784,
+        2.995034694671631,
+        3.3333518505096436,
+        0.0,
+        0.11092565208673477,
+        0.318450003862381,
+        1.0
+      ],
+      "min": [
+        -0.3494536280632019,
+        0.0,
+        0.0,
+        -1.7953904867172241,
+        -0.13677525520324707,
+        -0.3176078498363495,
+        0.0,
+        -0.0804772898554802,
+        0.0,
+        0.0,
+        -1.8121548891067505,
+        -0.21740999817848206,
+        -0.8863733410835266,
+        0.0
+      ],
+      "q01": [
+        -0.3341374397277832,
+        0.0,
+        0.0,
+        -1.6472991704940796,
+        -0.11959030479192734,
+        -0.30239319801330566,
+        0.0,
+        -0.0527563564479351,
+        0.0,
+        0.0,
+        -1.6625858545303345,
+        -0.19942482709884643,
+        -0.830798065662384,
+        0.0
+      ],
+      "q99": [
+        0.2560995936393735,
+        2.8609605312347384,
+        3.1173167228698704,
+        1.3075437545776367,
+        1.0580827236175536,
+        0.9104878306388855,
+        1.0,
+        0.31480422616004944,
+        2.913422060012817,
+        3.2138250827789308,
+        0.0,
+        0.09690605103969574,
+        0.31249096989631653,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.03136930614709854,
+        0.9640669822692871,
+        0.8403865694999695,
+        -0.423330694437027,
+        0.0556751973927021,
+        0.09843266755342484,
+        0.7630919218063354,
+        0.042088694870471954,
+        1.0294307470321655,
+        0.939507782459259,
+        -0.5354413986206055,
+        -0.014315903186798096,
+        -0.09174654632806778,
+        0.7476586103439331
+      ],
+      "std": [
+        0.11126016825437546,
+        1.1112853288650513,
+        1.0116392374038696,
+        0.6310778856277466,
+        0.1844586580991745,
+        0.2667086720466614,
+        0.40844425559043884,
+        0.10545298457145691,
+        1.1204065084457397,
+        1.0435622930526733,
+        0.5897011756896973,
+        0.05830603092908859,
+        0.2768862843513489,
+        0.41657936573028564
+      ],
+      "max": [
+        0.3149413466453552,
+        3.0223748683929443,
+        3.342350721359253,
+        1.4929821491241455,
+        1.153774380683899,
+        0.9257752299308777,
+        1.0,
+        0.32490795850753784,
+        2.995034694671631,
+        3.3333518505096436,
+        0.0,
+        0.11092565208673477,
+        0.318450003862381,
+        1.0
+      ],
+      "min": [
+        -0.3494536280632019,
+        0.0,
+        0.0,
+        -1.7953904867172241,
+        -0.13677525520324707,
+        -0.3176078498363495,
+        0.0,
+        -0.0804772898554802,
+        0.0,
+        0.0,
+        -1.8121548891067505,
+        -0.21740999817848206,
+        -0.8863733410835266,
+        0.0
+      ],
+      "q01": [
+        -0.3341374397277832,
+        0.0,
+        0.0,
+        -1.6472991704940796,
+        -0.11959030479192734,
+        -0.30239319801330566,
+        0.0,
+        -0.0527563564479351,
+        0.0,
+        0.0,
+        -1.6625858545303345,
+        -0.19942482709884643,
+        -0.830798065662384,
+        0.0
+      ],
+      "q99": [
+        0.2560995936393735,
+        2.8609605312347384,
+        3.1173167228698704,
+        1.3057452917098997,
+        1.0580827236175536,
+        0.9104878306388855,
+        1.0,
+        0.31480422616004944,
+        2.913422060012817,
+        3.2138250827789308,
+        0.0,
+        0.09655882418155662,
+        0.3124178946018219,
+        1.0
+      ]
+    },
+    "num_transitions": 7581,
+    "num_trajectories": 50
+  },
+  "open_laptop_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.412982702255249,
+        0.9725632071495056,
+        0.5560179352760315,
+        -0.018646035343408585,
+        -0.1403660625219345,
+        0.671284019947052,
+        0.5757868885993958,
+        0.3011881411075592,
+        0.795698344707489,
+        0.4804641604423523,
+        -0.04225534945726395,
+        0.059194255620241165,
+        0.7393441200256348,
+        0.648875892162323
+      ],
+      "std": [
+        0.41707736253738403,
+        0.9429622888565063,
+        0.6425602436065674,
+        0.4222852289676666,
+        0.2817792296409607,
+        0.6755929589271545,
+        0.48893213272094727,
+        0.3684118092060089,
+        0.9421394467353821,
+        0.6803411245346069,
+        0.4267505407333374,
+        0.22786928713321686,
+        0.8749793171882629,
+        0.47337010502815247
+      ],
+      "max": [
+        0.014050900936126709,
+        2.7143473625183105,
+        2.9012439250946045,
+        1.1832900047302246,
+        0.8149839043617249,
+        2.0226521492004395,
+        1.0,
+        1.3892728090286255,
+        2.72947359085083,
+        2.906212091445923,
+        1.3124799728393555,
+        1.0027532577514648,
+        2.6277825832366943,
+        1.0
+      ],
+      "min": [
+        -1.3947231769561768,
+        0.0,
+        0.0,
+        -1.5809979438781738,
+        -1.1472011804580688,
+        0.0,
+        0.0,
+        -0.005201177205890417,
+        0.0,
+        -0.009437700733542442,
+        -1.6318897008895874,
+        -0.7902398705482483,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -1.213874478340149,
+        0.0,
+        0.0,
+        -1.27146577835083,
+        -0.886477215886116,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3470568656921387,
+        -0.5466296076774597,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.417337417602539,
+        2.35109806060791,
+        0.9501821488142007,
+        0.5932847791910171,
+        1.9652117490768433,
+        1.0,
+        1.1480320692062378,
+        2.519510269165039,
+        2.53169584274292,
+        1.0674813234806055,
+        0.8172210627794251,
+        2.301086187362671,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.41133424639701843,
+        0.9674974679946899,
+        0.5518006682395935,
+        -0.016732074320316315,
+        -0.1401025503873825,
+        0.6682498455047607,
+        0.5781423449516296,
+        0.30009010434150696,
+        0.7918621301651001,
+        0.4770472049713135,
+        -0.040619634091854095,
+        0.059146538376808167,
+        0.7364427447319031,
+        0.6505815386772156
+      ],
+      "std": [
+        0.41728851199150085,
+        0.9423418641090393,
+        0.6399604678153992,
+        0.4200330376625061,
+        0.2817077338695526,
+        0.6755438446998596,
+        0.48856765031814575,
+        0.36834537982940674,
+        0.9407066106796265,
+        0.6772210597991943,
+        0.424578994512558,
+        0.22778856754302979,
+        0.8745430111885071,
+        0.47281336784362793
+      ],
+      "max": [
+        0.014050900936126709,
+        2.7143473625183105,
+        2.9012439250946045,
+        1.1832900047302246,
+        0.8149839043617249,
+        2.0226521492004395,
+        1.0,
+        1.3892728090286255,
+        2.72947359085083,
+        2.906212091445923,
+        1.3124799728393555,
+        1.0027532577514648,
+        2.6277825832366943,
+        1.0
+      ],
+      "min": [
+        -1.3947231769561768,
+        0.0,
+        0.0,
+        -1.5809979438781738,
+        -1.1472011804580688,
+        0.0,
+        0.0,
+        -0.005201177205890417,
+        0.0,
+        -0.009437700733542442,
+        -1.6318897008895874,
+        -0.7902398705482483,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -1.213874478340149,
+        0.0,
+        0.0,
+        -1.27146577835083,
+        -0.886477215886116,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3470568656921387,
+        -0.5466296076774597,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.417337417602539,
+        2.35109806060791,
+        0.9501821488142007,
+        0.5932847791910171,
+        1.9652117490768433,
+        1.0,
+        1.1480320692062378,
+        2.519510269165039,
+        2.53169584274292,
+        1.0674813234806055,
+        0.8172210627794251,
+        2.301086187362671,
+        1.0
+      ]
+    },
+    "num_transitions": 12312,
+    "num_trajectories": 50
+  },
+  "pick_dual_bottles_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.15950368344783783,
+        1.7896647453308105,
+        0.9439552426338196,
+        0.7748593091964722,
+        0.5115674138069153,
+        0.05964143946766853,
+        0.49804794788360596,
+        0.1409446895122528,
+        1.8259799480438232,
+        1.0187904834747314,
+        0.7640450596809387,
+        -0.5400498509407043,
+        -0.017349114641547203,
+        0.49804794788360596
+      ],
+      "std": [
+        0.20802833139896393,
+        0.7114025950431824,
+        0.49483397603034973,
+        0.31811821460723877,
+        0.31533852219581604,
+        0.07191983610391617,
+        0.48165053129196167,
+        0.2233102172613144,
+        0.717391312122345,
+        0.5089762806892395,
+        0.29336756467819214,
+        0.3482920825481415,
+        0.050167474895715714,
+        0.48165053129196167
+      ],
+      "max": [
+        0.39062753319740295,
+        2.8961520195007324,
+        2.413680076599121,
+        1.6144700050354004,
+        1.4948179721832275,
+        0.6830681562423706,
+        1.0,
+        0.6129000186920166,
+        2.735783815383911,
+        2.0820229053497314,
+        1.28603994846344,
+        0.0,
+        0.05678669363260269,
+        1.0
+      ],
+      "min": [
+        -0.5722799897193909,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.04302142560482025,
+        0.0,
+        -0.3316712975502014,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.37959623336792,
+        -0.302682101726532,
+        0.0
+      ],
+      "q01": [
+        -0.5586331933736801,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.010444268118590117,
+        0.0,
+        -0.26194658875465393,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.2651588320732117,
+        -0.17471149563789368,
+        0.0
+      ],
+      "q99": [
+        0.2784392535686493,
+        2.7243048548698425,
+        1.9894878566265106,
+        1.431083768606186,
+        1.2972519397735596,
+        0.3371240422129631,
+        1.0,
+        0.5960070490837097,
+        2.733114242553711,
+        2.0299019813537598,
+        1.236779510974884,
+        0.0,
+        0.04197721276432276,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.15498977899551392,
+        1.7792388200759888,
+        0.9396310448646545,
+        0.769199013710022,
+        0.5095281600952148,
+        0.0594169907271862,
+        0.5062099099159241,
+        0.13609662652015686,
+        1.814998984336853,
+        1.0140470266342163,
+        0.7581976652145386,
+        -0.538648784160614,
+        -0.01742432452738285,
+        0.5062099099159241
+      ],
+      "std": [
+        0.20542342960834503,
+        0.7279935479164124,
+        0.5007017254829407,
+        0.32545188069343567,
+        0.3178229331970215,
+        0.07205121219158173,
+        0.4816075265407562,
+        0.21984270215034485,
+        0.7347503900527954,
+        0.5156906247138977,
+        0.3011578619480133,
+        0.35011228919029236,
+        0.050126101821660995,
+        0.4816075265407562
+      ],
+      "max": [
+        0.39062753319740295,
+        2.8961520195007324,
+        2.413680076599121,
+        1.6144700050354004,
+        1.4948179721832275,
+        0.6830681562423706,
+        1.0,
+        0.6129000186920166,
+        2.735783815383911,
+        2.0820229053497314,
+        1.28603994846344,
+        0.0,
+        0.05678669363260269,
+        1.0
+      ],
+      "min": [
+        -0.5722799897193909,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -0.04302142560482025,
+        0.0,
+        -0.3316712975502014,
+        0.0,
+        0.0,
+        0.0,
+        -1.37959623336792,
+        -0.302682101726532,
+        0.0
+      ],
+      "q01": [
+        -0.5585764646530151,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        0.0,
+        -0.010444268118590117,
+        0.0,
+        -0.26194658875465393,
+        0.0,
+        9.999999747378752e-06,
+        0.0,
+        -1.2651588320732117,
+        -0.17471149563789368,
+        0.0
+      ],
+      "q99": [
+        0.2784392535686493,
+        2.7243048548698425,
+        1.9894878566265106,
+        1.431083768606186,
+        1.2972519397735596,
+        0.3371240422129631,
+        1.0,
+        0.5959392786026001,
+        2.733114242553711,
+        2.0299019813537598,
+        1.236779510974884,
+        0.0,
+        0.04197721276432276,
+        1.0
+      ]
+    },
+    "num_transitions": 6126,
+    "num_trajectories": 50
+  },
+  "place_dual_shoes_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.364918053150177,
+        1.520833969116211,
+        1.0176814794540405,
+        -0.6050214767456055,
+        0.016822682693600655,
+        -0.1008746474981308,
+        0.6267212629318237,
+        0.1734830141067505,
+        1.9947360754013062,
+        1.4386961460113525,
+        -0.9159395098686218,
+        -0.003797657787799835,
+        -0.27571171522140503,
+        0.31392714381217957
+      ],
+      "std": [
+        1.027938961982727,
+        0.9228556752204895,
+        0.6396123766899109,
+        0.3982831537723541,
+        0.05350078269839287,
+        1.1809768676757812,
+        0.4633759558200836,
+        0.302899032831192,
+        0.5275591015815735,
+        0.43367037177085876,
+        0.289938747882843,
+        0.02490071952342987,
+        1.0572752952575684,
+        0.44288790225982666
+      ],
+      "max": [
+        5.369949817657471,
+        2.5988383293151855,
+        2.2322678565979004,
+        2.1778516384074464e-05,
+        0.27490007877349854,
+        3.83882999420166,
+        1.0,
+        0.7725619673728943,
+        2.6282360553741455,
+        2.372544288635254,
+        0.0,
+        0.07164999842643738,
+        2.342289924621582,
+        1.0
+      ],
+      "min": [
+        -7.180570602416992,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4090766906738281,
+        -0.10993228107690811,
+        -2.4864256381988525,
+        0.0,
+        -0.2831000089645386,
+        0.0,
+        0.0,
+        -1.5554989576339722,
+        -0.13825319707393646,
+        -4.031702518463135,
+        0.0
+      ],
+      "q01": [
+        -6.867993459701538,
+        2.9715695291088196e-06,
+        -3.064708653255366e-05,
+        -1.2694409608840942,
+        -0.06486005619168282,
+        -2.466008424758911,
+        0.0,
+        -0.26483604311943054,
+        0.009487488549202682,
+        0.009014308238402009,
+        -1.411148397922516,
+        -0.11009304970502853,
+        -3.9720680713653564,
+        0.0
+      ],
+      "q99": [
+        1.2002487826347954,
+        2.494779109954834,
+        1.9488718807697298,
+        2.1778516384074464e-05,
+        0.2206193839013577,
+        3.605363287925731,
+        1.0,
+        0.7506802678108215,
+        2.5776290893554688,
+        2.192576406002045,
+        -0.005188394398428484,
+        0.057537742741406016,
+        2.3226377964019775,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.3650052845478058,
+        1.520833969116211,
+        1.0176814794540405,
+        -0.6050214767456055,
+        0.016822639852762222,
+        -0.1008746474981308,
+        0.6267212629318237,
+        0.17031559348106384,
+        1.9842966794967651,
+        1.4307619333267212,
+        -0.9114848971366882,
+        -0.0037371169310063124,
+        -0.2758965492248535,
+        0.31392714381217957
+      ],
+      "std": [
+        1.0279076099395752,
+        0.9228557348251343,
+        0.6396121978759766,
+        0.3982830345630646,
+        0.05350079387426376,
+        1.1809768676757812,
+        0.4633759558200836,
+        0.30088669061660767,
+        0.5429739952087402,
+        0.4430801570415497,
+        0.29594969749450684,
+        0.02473057620227337,
+        1.0498296022415161,
+        0.44288793206214905
+      ],
+      "max": [
+        5.369949817657471,
+        2.5988383293151855,
+        2.2322678565979004,
+        2.1778516384074464e-05,
+        0.27490007877349854,
+        3.83882999420166,
+        1.0,
+        0.7725619673728943,
+        2.6282360553741455,
+        2.372544288635254,
+        0.0,
+        0.07164999842643738,
+        2.342289924621582,
+        1.0
+      ],
+      "min": [
+        -7.180570602416992,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4090766906738281,
+        -0.10993228107690811,
+        -2.4864256381988525,
+        0.0,
+        -0.2831000089645386,
+        0.0,
+        0.0,
+        -1.5554989576339722,
+        -0.13825319707393646,
+        -4.031702518463135,
+        0.0
+      ],
+      "q01": [
+        -6.867993459701538,
+        0.0,
+        -3.064708653255366e-05,
+        -1.2694409608840942,
+        -0.06486005619168282,
+        -2.466008424758911,
+        0.0,
+        -0.26483604311943054,
+        0.0,
+        0.0,
+        -1.411148397922516,
+        -0.11009304970502853,
+        -3.9709057807922363,
+        0.0
+      ],
+      "q99": [
+        1.2002487826347954,
+        2.494779109954834,
+        1.9488718807697298,
+        2.1778516384074464e-05,
+        0.2206193839013577,
+        3.605363287925731,
+        1.0,
+        0.7506802678108215,
+        2.5776290893554688,
+        2.192576406002045,
+        0.0,
+        0.056274176910520335,
+        2.3226377964019775,
+        1.0
+      ]
+    },
+    "num_transitions": 11474,
+    "num_trajectories": 50
+  },
+  "place_object_basket_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.17315083742141724,
+        1.1054847240447998,
+        0.9055297374725342,
+        -0.3234107792377472,
+        0.21469952166080475,
+        0.17225050926208496,
+        0.7572181820869446,
+        0.16253036260604858,
+        0.9573038816452026,
+        0.8219836950302124,
+        -0.3597754240036011,
+        -0.10522812604904175,
+        -0.17085255682468414,
+        0.7928103804588318
+      ],
+      "std": [
+        0.21726401150226593,
+        1.0674771070480347,
+        0.9510499238967896,
+        0.7475572824478149,
+        0.4299476444721222,
+        0.44102591276168823,
+        0.4120478928089142,
+        0.3323862552642822,
+        1.0525022745132446,
+        0.9476361870765686,
+        0.7507379055023193,
+        0.3281928598880768,
+        0.524770200252533,
+        0.38940587639808655
+      ],
+      "max": [
+        0.02667570859193802,
+        3.778841733932495,
+        4.568304061889648,
+        1.0729998350143433,
+        1.2834042310714722,
+        1.6689540147781372,
+        1.0,
+        0.8685938715934753,
+        2.6522276401519775,
+        3.264349937438965,
+        1.1015950441360474,
+        1.1631274223327637,
+        3.160046100616455,
+        1.0
+      ],
+      "min": [
+        -0.858056902885437,
+        0.0,
+        -3.064708653255366e-05,
+        -1.8265033960342407,
+        -0.028474153950810432,
+        -0.8472005128860474,
+        0.0,
+        -2.803896188735962,
+        -0.4660651385784149,
+        -2.5819708753260784e-05,
+        -1.9893873929977417,
+        -1.1286083459854126,
+        -1.736908197402954,
+        0.0
+      ],
+      "q01": [
+        -0.6967605352401733,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7222540175914764,
+        -0.014083670675754547,
+        -0.5925237935781479,
+        0.0,
+        -0.04488968480378389,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7749713563919067,
+        -1.1208465099334717,
+        -1.7055805158615112,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.777959144115448,
+        2.902973175048828,
+        1.0099876379966732,
+        1.272336483001709,
+        1.5119220542907714,
+        1.0,
+        0.8364987969398499,
+        2.5737154483795166,
+        2.664341926574707,
+        1.0096371030807485,
+        0.11334303751587624,
+        0.8461964726448059,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.17225174605846405,
+        1.101753830909729,
+        0.9016473889350891,
+        -0.3206050992012024,
+        0.21468140184879303,
+        0.17057755589485168,
+        0.7589125037193298,
+        0.1611548811197281,
+        0.9520825743675232,
+        0.8164799809455872,
+        -0.35582447052001953,
+        -0.10522229224443436,
+        -0.16855837404727936,
+        0.7951502203941345
+      ],
+      "std": [
+        0.21675990521907806,
+        1.0675125122070312,
+        0.9500465393066406,
+        0.7456965446472168,
+        0.4299568831920624,
+        0.43978309631347656,
+        0.4109635651111603,
+        0.3318731486797333,
+        1.0517019033432007,
+        0.9455519914627075,
+        0.7481663227081299,
+        0.32819226384162903,
+        0.5233535766601562,
+        0.3876449465751648
+      ],
+      "max": [
+        0.02667570859193802,
+        3.778841733932495,
+        4.568304061889648,
+        1.0729998350143433,
+        1.2834042310714722,
+        1.6689540147781372,
+        1.0,
+        0.8685938715934753,
+        2.6522276401519775,
+        3.264349937438965,
+        1.1015950441360474,
+        1.1631274223327637,
+        3.160046100616455,
+        1.0
+      ],
+      "min": [
+        -0.858056902885437,
+        0.0,
+        -3.064708653255366e-05,
+        -1.8265033960342407,
+        -0.028474153950810432,
+        -0.8472005128860474,
+        0.0,
+        -2.803896188735962,
+        -0.4660651385784149,
+        -2.5819708753260784e-05,
+        -1.9893873929977417,
+        -1.1286083459854126,
+        -1.736908197402954,
+        0.0
+      ],
+      "q01": [
+        -0.6967605352401733,
+        0.0,
+        -3.064708653255366e-05,
+        -1.720620036125183,
+        -0.014083670675754547,
+        -0.5925237935781479,
+        0.0,
+        -0.04488968480378389,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7743152236938478,
+        -1.1208465099334717,
+        -1.7055805158615112,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.777959144115448,
+        2.902973175048828,
+        1.0099876379966732,
+        1.272336483001709,
+        1.5119220542907714,
+        1.0,
+        0.8364987969398499,
+        2.5737154483795166,
+        2.664341926574707,
+        1.0096371030807485,
+        0.11334303751587624,
+        0.8461964726448059,
+        1.0
+      ]
+    },
+    "num_transitions": 12394,
+    "num_trajectories": 50
+  },
+  "place_phone_stand_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.24955666065216064,
+        0.96904057264328,
+        0.6381116509437561,
+        -0.24100831151008606,
+        -0.11241041123867035,
+        -0.17180098593235016,
+        0.7342271208763123,
+        0.18775422871112823,
+        0.7277160286903381,
+        0.4949023425579071,
+        -0.1834067404270172,
+        0.0859995111823082,
+        0.13992515206336975,
+        0.8067823052406311
+      ],
+      "std": [
+        0.27407583594322205,
+        0.9312117695808411,
+        0.6364781856536865,
+        0.3659789264202118,
+        0.20980769395828247,
+        0.30180302262306213,
+        0.41827327013015747,
+        0.25742045044898987,
+        0.9351555705070496,
+        0.653581440448761,
+        0.33380553126335144,
+        0.19428309798240662,
+        0.28798776865005493,
+        0.3758675456047058
+      ],
+      "max": [
+        0.0,
+        2.447045087814331,
+        2.13200044631958,
+        0.27845299243927,
+        0.08664697408676147,
+        0.3559088408946991,
+        1.0,
+        0.881425678730011,
+        2.4448471069335938,
+        2.0905659198760986,
+        0.17398181557655334,
+        0.8084141612052917,
+        1.3334383964538574,
+        1.0
+      ],
+      "min": [
+        -0.9803099632263184,
+        0.0,
+        0.0,
+        -1.340118169784546,
+        -0.8613136410713196,
+        -1.3788976669311523,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2530916929244995,
+        -0.021880000829696655,
+        -0.4965520203113556,
+        0.0
+      ],
+      "q01": [
+        -0.8396719813346862,
+        0.0,
+        0.0,
+        -1.0591385531425477,
+        -0.7201460123062133,
+        -1.0765761065483093,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0728944265842437,
+        -0.015176629358902573,
+        -0.27935048937797546,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.360727548599243,
+        1.9245160818099976,
+        0.2115602046251297,
+        0.005798054859042168,
+        0.3227096199989319,
+        1.0,
+        0.7152422976493827,
+        2.348403215408325,
+        1.9352505207061768,
+        0.14278633892536163,
+        0.6668316721916199,
+        1.122056484222412,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.2470388114452362,
+        0.959843635559082,
+        0.6315163373947144,
+        -0.2413448542356491,
+        -0.11027531325817108,
+        -0.17068681120872498,
+        0.7342271208763123,
+        0.18596167862415314,
+        0.7207176685333252,
+        0.48970282077789307,
+        -0.18350425362586975,
+        0.08433938771486282,
+        0.13906404376029968,
+        0.8067823052406311
+      ],
+      "std": [
+        0.2737008035182953,
+        0.9307196736335754,
+        0.635271430015564,
+        0.365650475025177,
+        0.20842938125133514,
+        0.30195745825767517,
+        0.41827327013015747,
+        0.2567901611328125,
+        0.9326195120811462,
+        0.6510723233222961,
+        0.33370161056518555,
+        0.19280125200748444,
+        0.2880008816719055,
+        0.3758675456047058
+      ],
+      "max": [
+        0.0,
+        2.447045087814331,
+        2.13200044631958,
+        0.27845299243927,
+        0.08664697408676147,
+        0.3559088408946991,
+        1.0,
+        0.881425678730011,
+        2.4448471069335938,
+        2.0905659198760986,
+        0.17398181557655334,
+        0.8084141612052917,
+        1.3334383964538574,
+        1.0
+      ],
+      "min": [
+        -0.9803099632263184,
+        0.0,
+        0.0,
+        -1.340118169784546,
+        -0.8613136410713196,
+        -1.3788976669311523,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2530916929244995,
+        -0.021880000829696655,
+        -0.4965520203113556,
+        0.0
+      ],
+      "q01": [
+        -0.8396719813346862,
+        0.0,
+        0.0,
+        -1.0591385531425477,
+        -0.7201460123062133,
+        -1.0765761065483093,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.0728944265842437,
+        -0.015176629358902573,
+        -0.27935048937797546,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.360727548599243,
+        1.9245160818099976,
+        0.2115602046251297,
+        0.005798054859042168,
+        0.3227096199989319,
+        1.0,
+        0.7152422976493827,
+        2.348403215408325,
+        1.929482665061947,
+        0.14278633892536163,
+        0.6668316721916199,
+        1.122056484222412,
+        1.0
+      ]
+    },
+    "num_transitions": 6340,
+    "num_trajectories": 50
+  },
+  "put_bottles_dustbin_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.10476312041282654,
+        1.279449462890625,
+        0.5679159164428711,
+        0.5262239575386047,
+        0.22428759932518005,
+        0.043430764228105545,
+        0.6643036603927612,
+        0.19642633199691772,
+        1.117813229560852,
+        0.7129987478256226,
+        0.34945419430732727,
+        -0.2572241723537445,
+        -0.014616748318076134,
+        0.6229138970375061
+      ],
+      "std": [
+        0.25748223066329956,
+        0.9038821458816528,
+        0.44731849431991577,
+        0.5311910510063171,
+        0.24035809934139252,
+        0.05469735711812973,
+        0.44946926832199097,
+        0.2710811495780945,
+        0.9252318739891052,
+        0.6049585342407227,
+        0.31488096714019775,
+        0.2908095717430115,
+        0.04302773252129555,
+        0.4715794026851654
+      ],
+      "max": [
+        0.37749066948890686,
+        2.792454242706299,
+        1.926819086074829,
+        1.7764710187911987,
+        1.1897499561309814,
+        0.26964041590690613,
+        1.0,
+        0.5676500201225281,
+        2.6290316581726074,
+        2.1055216789245605,
+        1.4273184537887573,
+        2.08674136956688e-05,
+        0.08746904134750366,
+        1.0
+      ],
+      "min": [
+        -0.5788599848747253,
+        -0.000386573257856071,
+        -0.005483707413077354,
+        -0.016073711216449738,
+        -0.0011532115750014782,
+        -0.05382127687335014,
+        0.0,
+        -0.5238198041915894,
+        -1.2773498383467086e-05,
+        -3.102514165220782e-05,
+        -1.7486241631559096e-05,
+        -1.2793614864349365,
+        -0.28817570209503174,
+        0.0
+      ],
+      "q01": [
+        -0.5524951219558716,
+        -4.5852862058382016e-06,
+        -3.064708653255366e-05,
+        -1.1295316653558984e-05,
+        -6.482168373622699e-06,
+        -0.017715569883584976,
+        0.0,
+        -0.3273748874664307,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7486239812569693e-05,
+        -1.061243543624878,
+        -0.16889580547809602,
+        0.0
+      ],
+      "q99": [
+        0.18636572360992432,
+        2.6412384510040283,
+        1.7621351480484009,
+        1.5735922956466664,
+        0.9350311040878294,
+        0.17157290935516348,
+        1.0,
+        0.5604766869544983,
+        2.5446243667602535,
+        1.8965028524398804,
+        1.0255632400512695,
+        0.0,
+        0.07489971071481705,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.10503867268562317,
+        1.2768235206604004,
+        0.566667914390564,
+        0.5260285139083862,
+        0.2240736186504364,
+        0.043224893510341644,
+        0.6643036603927612,
+        0.19641055166721344,
+        1.117813229560852,
+        0.7129987478256226,
+        0.34945419430732727,
+        -0.2572241723537445,
+        -0.014616748318076134,
+        0.6229138970375061
+      ],
+      "std": [
+        0.257255882024765,
+        0.9052372574806213,
+        0.44780004024505615,
+        0.5313783884048462,
+        0.2404986470937729,
+        0.05462057143449783,
+        0.4494692385196686,
+        0.27109846472740173,
+        0.9252316355705261,
+        0.6049585342407227,
+        0.31488096714019775,
+        0.2908095717430115,
+        0.04302773252129555,
+        0.4715794026851654
+      ],
+      "max": [
+        0.37749066948890686,
+        2.792454242706299,
+        1.926819086074829,
+        1.7764710187911987,
+        1.1897499561309814,
+        0.26964041590690613,
+        1.0,
+        0.5676500201225281,
+        2.6290316581726074,
+        2.1055216789245605,
+        1.4273184537887573,
+        2.08674136956688e-05,
+        0.08746904134750366,
+        1.0
+      ],
+      "min": [
+        -0.5788599848747253,
+        -0.000386573257856071,
+        -0.005483707413077354,
+        -0.016073711216449738,
+        -0.0011532115750014782,
+        -0.05382127687335014,
+        0.0,
+        -0.5238198041915894,
+        -1.2773498383467086e-05,
+        -3.102514165220782e-05,
+        -1.7486241631559096e-05,
+        -1.2793614864349365,
+        -0.28817570209503174,
+        0.0
+      ],
+      "q01": [
+        -0.5524951219558716,
+        -4.5852862058382016e-06,
+        -3.064708653255366e-05,
+        -1.1295316653558984e-05,
+        -6.482168373622699e-06,
+        -0.017715569883584976,
+        0.0,
+        -0.3273748874664307,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.7486239812569693e-05,
+        -1.061243543624878,
+        -0.16889580547809602,
+        0.0
+      ],
+      "q99": [
+        0.18636572360992432,
+        2.6412384510040283,
+        1.7621351480484009,
+        1.5735922956466664,
+        0.9350311040878294,
+        0.17157290935516348,
+        1.0,
+        0.5604766869544983,
+        2.5446243667602535,
+        1.8965028524398804,
+        1.0255632400512695,
+        0.0,
+        0.07489971071481705,
+        1.0
+      ]
+    },
+    "num_transitions": 31457,
+    "num_trajectories": 50
+  },
+  "put_object_cabinet_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.28438690304756165,
+        1.764165997505188,
+        1.1697032451629639,
+        -0.8346169590950012,
+        -0.027913035824894905,
+        0.2120014876127243,
+        0.2626643180847168,
+        0.7117581367492676,
+        1.5304840803146362,
+        0.6800471544265747,
+        0.7674376368522644,
+        0.6864986419677734,
+        -0.9722089767456055,
+        0.4657360017299652
+      ],
+      "std": [
+        0.24526149034500122,
+        0.512263298034668,
+        0.6820644736289978,
+        0.5534589886665344,
+        0.1659320443868637,
+        0.859703004360199,
+        0.421505331993103,
+        0.5065756440162659,
+        0.9997316002845764,
+        0.4915132224559784,
+        0.6616247296333313,
+        0.5067867636680603,
+        0.763378381729126,
+        0.490205854177475
+      ],
+      "max": [
+        0.08587173372507095,
+        2.7109200954437256,
+        3.1437699794769287,
+        1.352994680404663,
+        0.03168417513370514,
+        1.9688830375671387,
+        1.0,
+        1.2608709335327148,
+        2.8519444465637207,
+        3.266448497772217,
+        1.3512014150619507,
+        1.2738021612167358,
+        0.7937034964561462,
+        1.0
+      ],
+      "min": [
+        -1.1788203716278076,
+        0.0,
+        0.0,
+        -1.865718960762024,
+        -1.1659986972808838,
+        -1.1686975955963135,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.9566882848739624,
+        -0.005439999978989363,
+        -1.559630036354065,
+        0.0
+      ],
+      "q01": [
+        -1.1621215343475342,
+        0.0,
+        0.0,
+        -1.8261551237106324,
+        -1.1501946449279785,
+        -1.1634292602539062,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3072348833084106,
+        -0.004720241297036409,
+        -1.554312481880188,
+        0.0
+      ],
+      "q99": [
+        0.07353582233190536,
+        2.61279296875,
+        2.9014140892028806,
+        1.318111538887024,
+        0.02163825549185276,
+        1.9688830375671387,
+        1.0,
+        1.2599315643310547,
+        2.408996105194092,
+        1.3551753759384155,
+        1.3495060205459595,
+        1.2695887088775635,
+        0.7937034964561462,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.2818736433982849,
+        1.7552909851074219,
+        1.161139726638794,
+        -0.8293845057487488,
+        -0.02772539108991623,
+        0.21406246721744537,
+        0.26281240582466125,
+        0.707289457321167,
+        1.5223047733306885,
+        0.6766162514686584,
+        0.7630574703216553,
+        0.6822458505630493,
+        -0.9668338298797607,
+        0.46929028630256653
+      ],
+      "std": [
+        0.24459987878799438,
+        0.5218791961669922,
+        0.6819084286689758,
+        0.5535831451416016,
+        0.16536164283752441,
+        0.8581108450889587,
+        0.42160049080848694,
+        0.5074564814567566,
+        1.0031490325927734,
+        0.4923613667488098,
+        0.6617483496665955,
+        0.5075437426567078,
+        0.7646470665931702,
+        0.49047353863716125
+      ],
+      "max": [
+        0.08587173372507095,
+        2.7109200954437256,
+        3.1437699794769287,
+        1.352994680404663,
+        0.03168417513370514,
+        1.9688830375671387,
+        1.0,
+        1.2608709335327148,
+        2.8519444465637207,
+        3.266448497772217,
+        1.3512014150619507,
+        1.2738021612167358,
+        0.7937034964561462,
+        1.0
+      ],
+      "min": [
+        -1.1788203716278076,
+        0.0,
+        0.0,
+        -1.865718960762024,
+        -1.1659986972808838,
+        -1.1686975955963135,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.9566882848739624,
+        -0.005439999978989363,
+        -1.559630036354065,
+        0.0
+      ],
+      "q01": [
+        -1.1621215343475342,
+        0.0,
+        0.0,
+        -1.8261551237106324,
+        -1.1501946449279785,
+        -1.1634292602539062,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.3071338891983033,
+        -0.004720241297036409,
+        -1.554312481880188,
+        0.0
+      ],
+      "q99": [
+        0.07353582233190536,
+        2.61279296875,
+        2.9014140892028806,
+        1.318111538887024,
+        0.021636107936501503,
+        1.9688830375671387,
+        1.0,
+        1.2599315643310547,
+        2.408996105194092,
+        1.3551753759384155,
+        1.3495060205459595,
+        1.2695887088775635,
+        0.7937034964561462,
+        1.0
+      ]
+    },
+    "num_transitions": 13505,
+    "num_trajectories": 50
+  },
+  "stack_blocks_two_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.21114705502986908,
+        0.9412369132041931,
+        0.7314395904541016,
+        -0.4761776030063629,
+        -0.0009380271076224744,
+        -0.07520589232444763,
+        0.7869701385498047,
+        0.26069143414497375,
+        1.1080588102340698,
+        0.8630656003952026,
+        -0.559351921081543,
+        0.007944311946630478,
+        0.08012033998966217,
+        0.7491208910942078
+      ],
+      "std": [
+        0.3050781488418579,
+        1.0409293174743652,
+        0.8131765127182007,
+        0.5270503759384155,
+        0.008852960541844368,
+        0.4395352900028229,
+        0.3933006227016449,
+        0.3120238780975342,
+        1.047856092453003,
+        0.8200691938400269,
+        0.5292724967002869,
+        0.023433445021510124,
+        0.47746512293815613,
+        0.41560548543930054
+      ],
+      "max": [
+        0.020010676234960556,
+        2.5165865421295166,
+        2.384333372116089,
+        2.1778516384074464e-05,
+        0.10835044085979462,
+        1.2322380542755127,
+        1.0,
+        0.8241801261901855,
+        2.444321393966675,
+        2.2086756229400635,
+        0.0,
+        0.18467359244823456,
+        0.8262989521026611,
+        1.0
+      ],
+      "min": [
+        -0.8325228095054626,
+        -4.1955649976443965e-06,
+        -3.064708653255366e-05,
+        -1.4605095386505127,
+        -0.0383315235376358,
+        -0.8282271027565002,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.7174886781722307e-05,
+        -1.3987765312194824,
+        -0.010218792594969273,
+        -1.1888049840927124,
+        0.0
+      ],
+      "q01": [
+        -0.8252553939819336,
+        0.0,
+        -3.064708653255366e-05,
+        -1.3362677097320557,
+        -0.026790249347686767,
+        -0.8190370202064514,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.340792179107666,
+        -0.006735008861869574,
+        -1.0526111125946045,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.4263905525207523,
+        2.078766918182373,
+        2.1778516384074464e-05,
+        0.019878851249814044,
+        1.0974429607391358,
+        1.0,
+        0.8126579523086548,
+        2.4025102615356446,
+        2.0527486324310305,
+        0.0,
+        0.15595661997795107,
+        0.8210508823394775,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.20990003645420074,
+        0.9378898739814758,
+        0.7284502387046814,
+        -0.47404396533966064,
+        -0.0009204319212585688,
+        -0.07391548156738281,
+        0.7869701385498047,
+        0.25938308238983154,
+        1.1046009063720703,
+        0.8598967790603638,
+        -0.557152271270752,
+        0.007918022572994232,
+        0.07882484793663025,
+        0.7491208910942078
+      ],
+      "std": [
+        0.3042908310890198,
+        1.0406111478805542,
+        0.8124014139175415,
+        0.526269793510437,
+        0.008842087350785732,
+        0.4385771155357361,
+        0.3933006227016449,
+        0.3114229738712311,
+        1.047912836074829,
+        0.8195027112960815,
+        0.5286997556686401,
+        0.023432398214936256,
+        0.47657015919685364,
+        0.41560545563697815
+      ],
+      "max": [
+        0.020010676234960556,
+        2.5165865421295166,
+        2.384333372116089,
+        2.1778516384074464e-05,
+        0.10835044085979462,
+        1.2322380542755127,
+        1.0,
+        0.8241801261901855,
+        2.444321393966675,
+        2.2086756229400635,
+        0.0,
+        0.18467359244823456,
+        0.8262989521026611,
+        1.0
+      ],
+      "min": [
+        -0.8325228095054626,
+        -4.1955649976443965e-06,
+        -3.064708653255366e-05,
+        -1.4605095386505127,
+        -0.0383315235376358,
+        -0.8282271027565002,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.7174886781722307e-05,
+        -1.3987765312194824,
+        -0.010218792594969273,
+        -1.1888049840927124,
+        0.0
+      ],
+      "q01": [
+        -0.8252553939819336,
+        0.0,
+        -3.064708653255366e-05,
+        -1.333071780204773,
+        -0.026790249347686767,
+        -0.8190370202064514,
+        0.0,
+        0.0,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.3284639596939087,
+        -0.006735008861869574,
+        -1.0526111125946045,
+        0.0
+      ],
+      "q99": [
+        0.020010676234960556,
+        2.4263905525207523,
+        2.078766918182373,
+        2.1778516384074464e-05,
+        0.019878851249814044,
+        1.0974429607391358,
+        1.0,
+        0.812503182888031,
+        2.4025102615356446,
+        2.0527486324310305,
+        0.0,
+        0.15595661997795107,
+        0.8210508823394775,
+        1.0
+      ]
+    },
+    "num_transitions": 15641,
+    "num_trajectories": 50
+  },
+  "stack_bowls_two_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.1218431144952774,
+        0.879644513130188,
+        0.6815189719200134,
+        -0.45526257157325745,
+        0.0535038486123085,
+        -0.09123744815587997,
+        0.7937045693397522,
+        0.1586911976337433,
+        1.0623502731323242,
+        0.8378229737281799,
+        -0.5510385036468506,
+        -0.0523577518761158,
+        0.17441710829734802,
+        0.7524454593658447
+      ],
+      "std": [
+        0.20389510691165924,
+        0.9881824851036072,
+        0.7890526652336121,
+        0.5182338953018188,
+        0.10292115807533264,
+        0.15442776679992676,
+        0.3886118233203888,
+        0.2241450399160385,
+        1.0105440616607666,
+        0.8408956527709961,
+        0.5377111434936523,
+        0.10513534396886826,
+        0.2661285400390625,
+        0.41391754150390625
+      ],
+      "max": [
+        0.10931042581796646,
+        2.699620485305786,
+        2.8983492851257324,
+        2.1778516384074464e-05,
+        0.3715807795524597,
+        0.09972000122070312,
+        1.0,
+        0.6297850608825684,
+        3.2168734073638916,
+        3.589359998703003,
+        0.0,
+        0.12872982025146484,
+        1.9198805093765259,
+        1.0
+      ],
+      "min": [
+        -0.583745002746582,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7004367113113403,
+        -0.019333183765411377,
+        -1.0937238931655884,
+        0.0,
+        -0.14522859454154968,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.9368948936462402,
+        -0.35954946279525757,
+        -0.08796866983175278,
+        0.0
+      ],
+      "q01": [
+        -0.5723252892494202,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4582831704616546,
+        -0.0065253665670752525,
+        -0.41102612018585205,
+        0.0,
+        -0.07947887197136878,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.6614161729812622,
+        -0.32789366245269774,
+        -0.06472839415073395,
+        0.0
+      ],
+      "q99": [
+        0.08905640229582787,
+        2.5206500244140626,
+        2.4590754604339606,
+        2.1778516384074464e-05,
+        0.3437314799427987,
+        0.09290612868964686,
+        1.0,
+        0.6060269474983215,
+        2.6554339265823366,
+        2.820633199214936,
+        0.0,
+        0.05726956911385072,
+        0.6783580780029297,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.12101656943559647,
+        0.8765714764595032,
+        0.6787510514259338,
+        -0.453287273645401,
+        0.05318109318614006,
+        -0.09065449237823486,
+        0.7937045693397522,
+        0.15774448215961456,
+        1.0592150688171387,
+        0.8349401950836182,
+        -0.5490079522132874,
+        -0.05208369344472885,
+        0.17349445819854736,
+        0.7524454593658447
+      ],
+      "std": [
+        0.20327353477478027,
+        0.9879177212715149,
+        0.7883735299110413,
+        0.5175690650939941,
+        0.10275426506996155,
+        0.15405894815921783,
+        0.3886118233203888,
+        0.2235718071460724,
+        1.0107218027114868,
+        0.8406001329421997,
+        0.5373188853263855,
+        0.10504182428121567,
+        0.26566997170448303,
+        0.41391754150390625
+      ],
+      "max": [
+        0.10931042581796646,
+        2.699620485305786,
+        2.8983492851257324,
+        2.1778516384074464e-05,
+        0.3715807795524597,
+        0.09972000122070312,
+        1.0,
+        0.6297850608825684,
+        3.2168734073638916,
+        3.589359998703003,
+        0.0,
+        0.12872982025146484,
+        1.9198805093765259,
+        1.0
+      ],
+      "min": [
+        -0.583745002746582,
+        0.0,
+        -3.064708653255366e-05,
+        -1.7004367113113403,
+        -0.019333183765411377,
+        -1.0937238931655884,
+        0.0,
+        -0.14522859454154968,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.9368948936462402,
+        -0.35954946279525757,
+        -0.08796866983175278,
+        0.0
+      ],
+      "q01": [
+        -0.5723252892494202,
+        0.0,
+        -3.064708653255366e-05,
+        -1.4582276344299316,
+        -0.0065253665670752525,
+        -0.41102612018585205,
+        0.0,
+        -0.07947887197136878,
+        -9.25191579881357e-06,
+        -2.394527655269485e-05,
+        -1.6614161729812622,
+        -0.32789366245269774,
+        -0.06472839415073395,
+        0.0
+      ],
+      "q99": [
+        0.08905640229582787,
+        2.5206500244140626,
+        2.4590754604339606,
+        2.1778516384074464e-05,
+        0.3437314799427987,
+        0.09290612868964686,
+        1.0,
+        0.6060269474983215,
+        2.6554339265823366,
+        2.820633199214936,
+        0.0,
+        0.05726956911385072,
+        0.6783580780029297,
+        1.0
+      ]
+    },
+    "num_transitions": 15948,
+    "num_trajectories": 50
+  }
+}

results/simvla_ts_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_ts_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_1-M2000-F2000-D1000/dataset_statistics.json

@@ -0,0 +1,218 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  }
+}

results/simvla_ts_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-5e-05+lora-r32+dropout-0.0--image_aug--simvla_ts_25_inner1_proj_type_relu_linear_ffn_type_relu_mlp_ffn_decoder_num_blocks_1-M2000-F2000-D1000/dataset_statistics.json

@@ -0,0 +1,218 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  }
+}

results/simvla_vr_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_vr_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_1-M2000-F2000-D1000/dataset_statistics.json

@@ -0,0 +1,218 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  }
+}

results/simvla_vr_25/openvla-7b+grab_roller_aloha_agilex_50+b4+lr-0.0001+lora-r32+dropout-0.0--image_aug--simvla_vr_25_inner1_proj_type_onlynorm_ffn_type_swiglu_mlp_ffn_decoder_num_blocks_4-M2000-F2000-D1000/dataset_statistics.json

@@ -0,0 +1,218 @@
+{
+  "grab_roller_aloha_agilex_50": {
+    "action": {
+      "mean": [
+        -0.7234118580818176,
+        1.632694959640503,
+        1.139991283416748,
+        -0.7696658968925476,
+        -0.004497084766626358,
+        -1.915460228919983,
+        0.6765856742858887,
+        0.4867003262042999,
+        1.567619800567627,
+        1.0547168254852295,
+        -0.740928053855896,
+        0.0007320955046452582,
+        1.6736525297164917,
+        0.6765856742858887
+      ],
+      "std": [
+        0.9490994811058044,
+        0.7159450650215149,
+        0.5567411184310913,
+        0.3428436517715454,
+        0.023813901469111443,
+        0.9220959544181824,
+        0.44203680753707886,
+        0.2887645363807678,
+        0.6954315304756165,
+        0.5172013640403748,
+        0.32894495129585266,
+        0.02310887910425663,
+        0.8557336330413818,
+        0.44203680753707886
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3311991214752197,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2851634454727172,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.1660122013092042,
+        0.0,
+        0.03330111399292946,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9841104364395143,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ],
+      "mask": [
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true,
+        true
+      ]
+    },
+    "proprio": {
+      "mean": [
+        -0.7144084572792053,
+        1.6121407747268677,
+        1.1227450370788574,
+        -0.7565815448760986,
+        -0.0046524289064109325,
+        -1.8903743028640747,
+        0.687203586101532,
+        0.4801884591579437,
+        1.5479238033294678,
+        1.0385884046554565,
+        -0.7280691862106323,
+        0.0008704152423888445,
+        1.651688575744629,
+        0.687203586101532
+      ],
+      "std": [
+        0.9468903541564941,
+        0.7339856028556824,
+        0.5653066039085388,
+        0.3482204079627991,
+        0.023658812046051025,
+        0.9399275779724121,
+        0.4376404583454132,
+        0.29170694947242737,
+        0.7125725150108337,
+        0.5250204801559448,
+        0.33385151624679565,
+        0.022993318736553192,
+        0.8697940111160278,
+        0.4376404583454132
+      ],
+      "max": [
+        0.0,
+        2.482893466949463,
+        2.353947639465332,
+        0.0,
+        0.05954868718981743,
+        0.0,
+        1.0,
+        1.1409132480621338,
+        2.443716287612915,
+        2.2799878120422363,
+        0.0,
+        0.15795069932937622,
+        3.4252119064331055,
+        1.0
+      ],
+      "min": [
+        -7.338869571685791,
+        0.0,
+        0.0,
+        -1.5001972913742065,
+        -0.12379012256860733,
+        -3.4617013931274414,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.385493516921997,
+        -0.048340000212192535,
+        0.0,
+        0.0
+      ],
+      "q01": [
+        -7.335215625762939,
+        0.0,
+        0.0,
+        -1.3265374326705932,
+        -0.11849832087755204,
+        -3.424123525619507,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        -1.2640411233901978,
+        -0.0435758301615715,
+        0.0,
+        0.0
+      ],
+      "q99": [
+        0.0,
+        2.478337526321411,
+        2.151273918151856,
+        0.0,
+        0.03329093188047409,
+        0.0,
+        1.0,
+        1.1318105459213257,
+        2.3855650424957275,
+        1.9724502563476562,
+        0.0,
+        0.11385425746440887,
+        3.409965982437134,
+        1.0
+      ]
+    },
+    "num_transitions": 4709,
+    "num_trajectories": 50
+  }
+}

run_scripts/ffn_q2a/aloha/aloha_robotwin2_ffn_50.sh

@@ -0,0 +1,108 @@
+#========== settings ==========#
+PROJECT_PATH=simvla_twin2
+ROOT_PATH=/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137
+#========== !NOTE! ==========#
+RUN_MODE=simvla_patch_all_25
+use_predict_future_prop=False
+batch_size=4
+use_action_ts_head=True
+use_one_embed=True
+use_multi_scaling=False
+mlp_type=ffn
+decoder_num_blocks=4
+robot_platform=aloha
+proj_type=onlynorm
+ffn_type=swiglu
+expand_inner_ratio=1
+linear_drop_ratio=0.0
+multi_queries_num=14
+multi_query_norm_type=layernorm
+action_norm=layernorm
+use_fredf=False
+use_patch_wise_loss=True
+use_dual_arm_head=False
+MODE=${RUN_MODE}_inner${expand_inner_ratio}_proj_type_${proj_type}_ffn_type_${ffn_type}_mlp_${mlp_type}_decoder_num_blocks_${decoder_num_blocks}
+#========== !NOTE! ==========#
+use_l1_regression=True
+num_images_in_input=3     
+wandb_entity=chenghaha
+wandb_project=robotwin
+wandb_log_freq=1
+use_proprio=True
+use_diffusion=False
+use_film=True
+num_steps_before_decay=10000
+save_freq=10000
+max_steps=20000
+vla_path=$ROOT_PATH/ai_models/openvla/openvla-7b
+data_root_dir=$ROOT_PATH/datasets/TianxingChen/RoboTwin2.0/tfds
+dataset_name=aloha_agilex_robotwin2_benchmark
+run_root_dir=$ROOT_PATH/vla_projects/$PROJECT_PATH/results/$RUN_MODE
+#========== get run_id ==========#
+note_parts=("${MODE}")
+
+# if [ "$use_l1_regression" = "True" ]; then
+#     note_parts+=("L1_regression")
+# fi
+
+# if [ "$num_images_in_input" == 1 ]; then
+#     note_parts+=("3rd_person_img")
+# else
+#     note_parts+=("3rd_person_img_and_wrist")
+# fi
+
+# if [ "$use_l1_regression" = "True" ]; then
+#     note_parts+=("proprio_state")
+# fi
+
+# if [ "$use_film" = "True" ]; then
+#     note_parts+=("Film")
+# fi
+note_parts+=("M$max_steps-F$save_freq-D$num_steps_before_decay")
+run_id_note_value=$(IFS='--'; echo "${note_parts[*]}")
+
+#========== enter environment ==========#
+conda activate openvla-oft
+cd $ROOT_PATH/vla_projects/$PROJECT_PATH
+export PYTHONPATH=$ROOT_PATH/vla_projects/$PROJECT_PATH
+
+#========== run ==========#
+WANDB_CONSOLE=off WANDB_MODE=offline torchrun --standalone --nnodes 1 --nproc-per-node 4 vla-scripts/finetune.py \
+  --vla_path "$vla_path" \
+  --data_root_dir "$data_root_dir" \
+  --dataset_name "$dataset_name" \
+  --run_root_dir "$run_root_dir" \
+  --use_l1_regression "$use_l1_regression" \
+  --use_diffusion "$use_diffusion" \
+  --use_film "$use_film" \
+  --num_images_in_input "$num_images_in_input" \
+  --use_proprio "$use_proprio" \
+  --batch_size "$batch_size" \
+  --learning_rate 1e-4 \
+  --num_steps_before_decay "$num_steps_before_decay" \
+  --max_steps "$max_steps" \
+  --save_freq "$save_freq" \
+  --save_latest_checkpoint_only False \
+  --image_aug True \
+  --lora_rank 32 \
+  --wandb_entity "$wandb_entity" \
+  --wandb_project "$wandb_project" \
+  --wandb_log_freq "$wandb_log_freq" \
+  --run_id_note "$run_id_note_value" \
+  --use_predict_future_prop "$use_predict_future_prop" \
+  --use_action_ts_head "$use_action_ts_head" \
+  --use_one_embed "$use_one_embed" \
+  --use_multi_scaling "$use_multi_scaling" \
+  --mlp_type "$mlp_type" \
+  --decoder_num_blocks "$decoder_num_blocks" \
+  --robot_platform "$robot_platform" \
+  --proj_type "$proj_type" \
+  --ffn_type "$ffn_type" \
+  --expand_inner_ratio "$expand_inner_ratio" \
+  --linear_drop_ratio "$linear_drop_ratio" \
+  --multi_query_norm_type "$multi_query_norm_type" \
+  --multi_queries_num "$multi_queries_num" \
+  --action_norm "$action_norm" \
+  --use_fredf "$use_fredf" \
+  --use_patch_wise_loss "$use_patch_wise_loss" \
+  --use_dual_arm_head "$use_dual_arm_head"

run_scripts/ffn_q2a/aloha/test_aloha_robotwin2_ffn_10.sh

@@ -0,0 +1,104 @@
+#========== settings ==========#
+PROJECT_PATH=simvla_twin2
+ROOT_PATH=/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137
+#========== !NOTE! ==========#
+RUN_MODE=simvla_two_10
+use_predict_future_prop=False
+batch_size=4
+use_action_ts_head=True
+use_one_embed=True
+use_multi_scaling=False
+mlp_type=ffn
+decoder_num_blocks=2
+robot_platform=10_al
+proj_type=onlynorm
+ffn_type=relu
+expand_inner_ratio=1
+linear_drop_ratio=0.0
+multi_queries_num=2
+multi_query_norm_type=layernorm
+action_norm=layernorm
+use_patch_wise_loss=True
+MODE=${RUN_MODE}_inner${expand_inner_ratio}_proj_type_${proj_type}_ffn_type_${ffn_type}_mlp_${mlp_type}_decoder_num_blocks_${decoder_num_blocks}
+#========== !NOTE! ==========#
+use_l1_regression=True
+num_images_in_input=3     
+wandb_entity=chenghaha
+wandb_project=robotwin
+wandb_log_freq=1
+use_proprio=True
+use_diffusion=False
+use_film=True
+num_steps_before_decay=1000
+save_freq=2000
+max_steps=2000
+vla_path=$ROOT_PATH/ai_models/openvla/openvla-7b
+data_root_dir=$ROOT_PATH/datasets/TianxingChen/RoboTwin2.0/tfds
+dataset_name=grab_roller_aloha_agilex_50
+run_root_dir=$ROOT_PATH/vla_projects/$PROJECT_PATH/results/$RUN_MODE
+#========== get run_id ==========#
+note_parts=("${MODE}")
+
+# if [ "$use_l1_regression" = "True" ]; then
+#     note_parts+=("L1_regression")
+# fi
+
+# if [ "$num_images_in_input" == 1 ]; then
+#     note_parts+=("3rd_person_img")
+# else
+#     note_parts+=("3rd_person_img_and_wrist")
+# fi
+
+# if [ "$use_l1_regression" = "True" ]; then
+#     note_parts+=("proprio_state")
+# fi
+
+# if [ "$use_film" = "True" ]; then
+#     note_parts+=("Film")
+# fi
+note_parts+=("M$max_steps-F$save_freq-D$num_steps_before_decay")
+run_id_note_value=$(IFS='--'; echo "${note_parts[*]}")
+
+#========== enter environment ==========#
+conda activate openvla-oft
+cd $ROOT_PATH/vla_projects/$PROJECT_PATH
+export PYTHONPATH=$ROOT_PATH/vla_projects/$PROJECT_PATH
+
+#========== run ==========#
+WANDB_CONSOLE=off WANDB_MODE=offline torchrun --standalone --nnodes 1 --nproc-per-node 4 vla-scripts/finetune.py \
+  --vla_path "$vla_path" \
+  --data_root_dir "$data_root_dir" \
+  --dataset_name "$dataset_name" \
+  --run_root_dir "$run_root_dir" \
+  --use_l1_regression "$use_l1_regression" \
+  --use_diffusion "$use_diffusion" \
+  --use_film "$use_film" \
+  --num_images_in_input "$num_images_in_input" \
+  --use_proprio "$use_proprio" \
+  --batch_size "$batch_size" \
+  --learning_rate 1e-4 \
+  --num_steps_before_decay "$num_steps_before_decay" \
+  --max_steps "$max_steps" \
+  --save_freq "$save_freq" \
+  --save_latest_checkpoint_only False \
+  --image_aug True \
+  --lora_rank 32 \
+  --wandb_entity "$wandb_entity" \
+  --wandb_project "$wandb_project" \
+  --wandb_log_freq "$wandb_log_freq" \
+  --run_id_note "$run_id_note_value" \
+  --use_predict_future_prop "$use_predict_future_prop" \
+  --use_action_ts_head "$use_action_ts_head" \
+  --use_one_embed "$use_one_embed" \
+  --use_multi_scaling "$use_multi_scaling" \
+  --mlp_type "$mlp_type" \
+  --decoder_num_blocks "$decoder_num_blocks" \
+  --robot_platform "$robot_platform" \
+  --proj_type "$proj_type" \
+  --ffn_type "$ffn_type" \
+  --expand_inner_ratio "$expand_inner_ratio" \
+  --linear_drop_ratio "$linear_drop_ratio" \
+  --multi_query_norm_type "$multi_query_norm_type" \
+  --multi_queries_num "$multi_queries_num" \
+  --action_norm "$action_norm" \
+  --use_patch_wise_loss "$use_patch_wise_loss" 

run_scripts/ffn_q2a/aloha/test_aloha_robotwin2_ffn_25_tcn.sh

@@ -0,0 +1,109 @@
+#========== settings ==========#
+PROJECT_PATH=simvla_twin2
+ROOT_PATH=/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137
+#========== !NOTE! ==========#
+RUN_MODE=simvla_tcn_25
+use_predict_future_prop=False
+batch_size=4
+use_action_ts_head=True
+use_one_embed=True
+use_multi_scaling=False
+mlp_type=ffn
+decoder_num_blocks=4
+robot_platform=aloha
+proj_type=onlynorm
+ffn_type=relu
+expand_inner_ratio=1
+linear_drop_ratio=0.0
+multi_queries_num=25
+multi_query_norm_type=layernorm
+action_norm=layernorm
+use_fredf=False
+use_patch_wise_loss=False
+use_dual_arm_head=False
+use_tcn_head=True
+MODE=${RUN_MODE}_inner${expand_inner_ratio}_proj_type_${proj_type}_ffn_type_${ffn_type}_mlp_${mlp_type}_decoder_num_blocks_${decoder_num_blocks}
+#========== !NOTE! ==========#
+use_l1_regression=True
+num_images_in_input=3     
+wandb_entity=chenghaha
+wandb_project=robotwin
+wandb_log_freq=1
+use_proprio=True
+use_diffusion=False
+use_film=True
+num_steps_before_decay=10000
+save_freq=10000
+max_steps=20000
+vla_path=$ROOT_PATH/ai_models/openvla/openvla-7b
+data_root_dir=$ROOT_PATH/datasets/TianxingChen/RoboTwin2.0/tfds
+dataset_name=aloha_agilex_robotwin2_benchmark
+run_root_dir=$ROOT_PATH/vla_projects/$PROJECT_PATH/results/$RUN_MODE
+#========== get run_id ==========#
+note_parts=("${MODE}")
+
+# if [ "$use_l1_regression" = "True" ]; then
+#     note_parts+=("L1_regression")
+# fi
+
+# if [ "$num_images_in_input" == 1 ]; then
+#     note_parts+=("3rd_person_img")
+# else
+#     note_parts+=("3rd_person_img_and_wrist")
+# fi
+
+# if [ "$use_l1_regression" = "True" ]; then
+#     note_parts+=("proprio_state")
+# fi
+
+# if [ "$use_film" = "True" ]; then
+#     note_parts+=("Film")
+# fi
+note_parts+=("M$max_steps-F$save_freq-D$num_steps_before_decay")
+run_id_note_value=$(IFS='--'; echo "${note_parts[*]}")
+
+#========== enter environment ==========#
+conda activate openvla-oft
+cd $ROOT_PATH/vla_projects/$PROJECT_PATH
+export PYTHONPATH=$ROOT_PATH/vla_projects/$PROJECT_PATH
+
+#========== run ==========#
+WANDB_CONSOLE=off WANDB_MODE=offline torchrun --standalone --nnodes 1 --nproc-per-node 4 vla-scripts/finetune.py \
+  --vla_path "$vla_path" \
+  --data_root_dir "$data_root_dir" \
+  --dataset_name "$dataset_name" \
+  --run_root_dir "$run_root_dir" \
+  --use_l1_regression "$use_l1_regression" \
+  --use_diffusion "$use_diffusion" \
+  --use_film "$use_film" \
+  --num_images_in_input "$num_images_in_input" \
+  --use_proprio "$use_proprio" \
+  --batch_size "$batch_size" \
+  --learning_rate 1e-4 \
+  --num_steps_before_decay "$num_steps_before_decay" \
+  --max_steps "$max_steps" \
+  --save_freq "$save_freq" \
+  --save_latest_checkpoint_only False \
+  --image_aug True \
+  --lora_rank 32 \
+  --wandb_entity "$wandb_entity" \
+  --wandb_project "$wandb_project" \
+  --wandb_log_freq "$wandb_log_freq" \
+  --run_id_note "$run_id_note_value" \
+  --use_predict_future_prop "$use_predict_future_prop" \
+  --use_action_ts_head "$use_action_ts_head" \
+  --use_one_embed "$use_one_embed" \
+  --use_multi_scaling "$use_multi_scaling" \
+  --mlp_type "$mlp_type" \
+  --decoder_num_blocks "$decoder_num_blocks" \
+  --robot_platform "$robot_platform" \
+  --proj_type "$proj_type" \
+  --ffn_type "$ffn_type" \
+  --expand_inner_ratio "$expand_inner_ratio" \
+  --linear_drop_ratio "$linear_drop_ratio" \
+  --multi_query_norm_type "$multi_query_norm_type" \
+  --multi_queries_num "$multi_queries_num" \
+  --action_norm "$action_norm" \
+  --use_fredf "$use_fredf" \
+  --use_patch_wise_loss "$use_patch_wise_loss" \
+  --use_dual_arm_head "$use_dual_arm_head"

vggt/heads/track_modules/base_track_predictor.py

@@ -0,0 +1,209 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+
+from .blocks import EfficientUpdateFormer, CorrBlock
+from .utils import sample_features4d, get_2d_embedding, get_2d_sincos_pos_embed
+from .modules import Mlp
+
+
+class BaseTrackerPredictor(nn.Module):
+    def __init__(
+        self,
+        stride=1,
+        corr_levels=5,
+        corr_radius=4,
+        latent_dim=128,
+        hidden_size=384,
+        use_spaceatt=True,
+        depth=6,
+        max_scale=518,
+        predict_conf=True,
+    ):
+        super(BaseTrackerPredictor, self).__init__()
+        """
+        The base template to create a track predictor
+        
+        Modified from https://github.com/facebookresearch/co-tracker/
+        and https://github.com/facebookresearch/vggsfm
+        """
+
+        self.stride = stride
+        self.latent_dim = latent_dim
+        self.corr_levels = corr_levels
+        self.corr_radius = corr_radius
+        self.hidden_size = hidden_size
+        self.max_scale = max_scale
+        self.predict_conf = predict_conf
+
+        self.flows_emb_dim = latent_dim // 2
+
+        self.corr_mlp = Mlp(
+            in_features=self.corr_levels * (self.corr_radius * 2 + 1) ** 2,
+            hidden_features=self.hidden_size,
+            out_features=self.latent_dim,
+        )
+
+        self.transformer_dim = self.latent_dim + self.latent_dim + self.latent_dim + 4
+
+        self.query_ref_token = nn.Parameter(torch.randn(1, 2, self.transformer_dim))
+
+        space_depth = depth if use_spaceatt else 0
+        time_depth = depth
+
+        self.updateformer = EfficientUpdateFormer(
+            space_depth=space_depth,
+            time_depth=time_depth,
+            input_dim=self.transformer_dim,
+            hidden_size=self.hidden_size,
+            output_dim=self.latent_dim + 2,
+            mlp_ratio=4.0,
+            add_space_attn=use_spaceatt,
+        )
+
+        self.fmap_norm = nn.LayerNorm(self.latent_dim)
+        self.ffeat_norm = nn.GroupNorm(1, self.latent_dim)
+
+        # A linear layer to update track feats at each iteration
+        self.ffeat_updater = nn.Sequential(nn.Linear(self.latent_dim, self.latent_dim), nn.GELU())
+
+        self.vis_predictor = nn.Sequential(nn.Linear(self.latent_dim, 1))
+
+        if predict_conf:
+            self.conf_predictor = nn.Sequential(nn.Linear(self.latent_dim, 1))
+
+    def forward(self, query_points, fmaps=None, iters=6, return_feat=False, down_ratio=1, apply_sigmoid=True):
+        """
+        query_points: B x N x 2, the number of batches, tracks, and xy
+        fmaps: B x S x C x HH x WW, the number of batches, frames, and feature dimension.
+                note HH and WW is the size of feature maps instead of original images
+        """
+        B, N, D = query_points.shape
+        B, S, C, HH, WW = fmaps.shape
+
+        assert D == 2, "Input points must be 2D coordinates"
+
+        # apply a layernorm to fmaps here
+        fmaps = self.fmap_norm(fmaps.permute(0, 1, 3, 4, 2))
+        fmaps = fmaps.permute(0, 1, 4, 2, 3)
+
+        # Scale the input query_points because we may downsample the images
+        # by down_ratio or self.stride
+        # e.g., if a 3x1024x1024 image is processed to a 128x256x256 feature map
+        # its query_points should be query_points/4
+        if down_ratio > 1:
+            query_points = query_points / float(down_ratio)
+
+        query_points = query_points / float(self.stride)
+
+        # Init with coords as the query points
+        # It means the search will start from the position of query points at the reference frames
+        coords = query_points.clone().reshape(B, 1, N, 2).repeat(1, S, 1, 1)
+
+        # Sample/extract the features of the query points in the query frame
+        query_track_feat = sample_features4d(fmaps[:, 0], coords[:, 0])
+
+        # init track feats by query feats
+        track_feats = query_track_feat.unsqueeze(1).repeat(1, S, 1, 1)  # B, S, N, C
+        # back up the init coords
+        coords_backup = coords.clone()
+
+        fcorr_fn = CorrBlock(fmaps, num_levels=self.corr_levels, radius=self.corr_radius)
+
+        coord_preds = []
+
+        # Iterative Refinement
+        for _ in range(iters):
+            # Detach the gradients from the last iteration
+            # (in my experience, not very important for performance)
+            coords = coords.detach()
+
+            fcorrs = fcorr_fn.corr_sample(track_feats, coords)
+
+            corr_dim = fcorrs.shape[3]
+            fcorrs_ = fcorrs.permute(0, 2, 1, 3).reshape(B * N, S, corr_dim)
+            fcorrs_ = self.corr_mlp(fcorrs_)
+
+            # Movement of current coords relative to query points
+            flows = (coords - coords[:, 0:1]).permute(0, 2, 1, 3).reshape(B * N, S, 2)
+
+            flows_emb = get_2d_embedding(flows, self.flows_emb_dim, cat_coords=False)
+
+            # (In my trials, it is also okay to just add the flows_emb instead of concat)
+            flows_emb = torch.cat([flows_emb, flows / self.max_scale, flows / self.max_scale], dim=-1)
+
+            track_feats_ = track_feats.permute(0, 2, 1, 3).reshape(B * N, S, self.latent_dim)
+
+            # Concatenate them as the input for the transformers
+            transformer_input = torch.cat([flows_emb, fcorrs_, track_feats_], dim=2)
+
+            # 2D positional embed
+            # TODO: this can be much simplified
+            pos_embed = get_2d_sincos_pos_embed(self.transformer_dim, grid_size=(HH, WW)).to(query_points.device)
+            sampled_pos_emb = sample_features4d(pos_embed.expand(B, -1, -1, -1), coords[:, 0])
+
+            sampled_pos_emb = rearrange(sampled_pos_emb, "b n c -> (b n) c").unsqueeze(1)
+
+            x = transformer_input + sampled_pos_emb
+
+            # Add the query ref token to the track feats
+            query_ref_token = torch.cat(
+                [self.query_ref_token[:, 0:1], self.query_ref_token[:, 1:2].expand(-1, S - 1, -1)], dim=1
+            )
+            x = x + query_ref_token.to(x.device).to(x.dtype)
+
+            # B, N, S, C
+            x = rearrange(x, "(b n) s d -> b n s d", b=B)
+
+            # Compute the delta coordinates and delta track features
+            delta, _ = self.updateformer(x)
+
+            # BN, S, C
+            delta = rearrange(delta, " b n s d -> (b n) s d", b=B)
+            delta_coords_ = delta[:, :, :2]
+            delta_feats_ = delta[:, :, 2:]
+
+            track_feats_ = track_feats_.reshape(B * N * S, self.latent_dim)
+            delta_feats_ = delta_feats_.reshape(B * N * S, self.latent_dim)
+
+            # Update the track features
+            track_feats_ = self.ffeat_updater(self.ffeat_norm(delta_feats_)) + track_feats_
+
+            track_feats = track_feats_.reshape(B, N, S, self.latent_dim).permute(0, 2, 1, 3)  # BxSxNxC
+
+            # B x S x N x 2
+            coords = coords + delta_coords_.reshape(B, N, S, 2).permute(0, 2, 1, 3)
+
+            # Force coord0 as query
+            # because we assume the query points should not be changed
+            coords[:, 0] = coords_backup[:, 0]
+
+            # The predicted tracks are in the original image scale
+            if down_ratio > 1:
+                coord_preds.append(coords * self.stride * down_ratio)
+            else:
+                coord_preds.append(coords * self.stride)
+
+        # B, S, N
+        vis_e = self.vis_predictor(track_feats.reshape(B * S * N, self.latent_dim)).reshape(B, S, N)
+        if apply_sigmoid:
+            vis_e = torch.sigmoid(vis_e)
+
+        if self.predict_conf:
+            conf_e = self.conf_predictor(track_feats.reshape(B * S * N, self.latent_dim)).reshape(B, S, N)
+            if apply_sigmoid:
+                conf_e = torch.sigmoid(conf_e)
+        else:
+            conf_e = None
+
+        if return_feat:
+            return coord_preds, vis_e, track_feats, query_track_feat, conf_e
+        else:
+            return coord_preds, vis_e, conf_e

vggt/heads/track_modules/blocks.py

@@ -0,0 +1,246 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+# Modified from https://github.com/facebookresearch/co-tracker/
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .utils import bilinear_sampler
+from .modules import Mlp, AttnBlock, CrossAttnBlock, ResidualBlock
+
+
+class EfficientUpdateFormer(nn.Module):
+    """
+    Transformer model that updates track estimates.
+    """
+
+    def __init__(
+        self,
+        space_depth=6,
+        time_depth=6,
+        input_dim=320,
+        hidden_size=384,
+        num_heads=8,
+        output_dim=130,
+        mlp_ratio=4.0,
+        add_space_attn=True,
+        num_virtual_tracks=64,
+    ):
+        super().__init__()
+
+        self.out_channels = 2
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.add_space_attn = add_space_attn
+
+        # Add input LayerNorm before linear projection
+        self.input_norm = nn.LayerNorm(input_dim)
+        self.input_transform = torch.nn.Linear(input_dim, hidden_size, bias=True)
+
+        # Add output LayerNorm before final projection
+        self.output_norm = nn.LayerNorm(hidden_size)
+        self.flow_head = torch.nn.Linear(hidden_size, output_dim, bias=True)
+        self.num_virtual_tracks = num_virtual_tracks
+
+        if self.add_space_attn:
+            self.virual_tracks = nn.Parameter(torch.randn(1, num_virtual_tracks, 1, hidden_size))
+        else:
+            self.virual_tracks = None
+
+        self.time_blocks = nn.ModuleList(
+            [
+                AttnBlock(
+                    hidden_size,
+                    num_heads,
+                    mlp_ratio=mlp_ratio,
+                    attn_class=nn.MultiheadAttention,
+                )
+                for _ in range(time_depth)
+            ]
+        )
+
+        if add_space_attn:
+            self.space_virtual_blocks = nn.ModuleList(
+                [
+                    AttnBlock(
+                        hidden_size,
+                        num_heads,
+                        mlp_ratio=mlp_ratio,
+                        attn_class=nn.MultiheadAttention,
+                    )
+                    for _ in range(space_depth)
+                ]
+            )
+            self.space_point2virtual_blocks = nn.ModuleList(
+                [CrossAttnBlock(hidden_size, hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(space_depth)]
+            )
+            self.space_virtual2point_blocks = nn.ModuleList(
+                [CrossAttnBlock(hidden_size, hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(space_depth)]
+            )
+            assert len(self.time_blocks) >= len(self.space_virtual2point_blocks)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+            torch.nn.init.trunc_normal_(self.flow_head.weight, std=0.001)
+
+        self.apply(_basic_init)
+
+    def forward(self, input_tensor, mask=None):
+        # Apply input LayerNorm
+        input_tensor = self.input_norm(input_tensor)
+        tokens = self.input_transform(input_tensor)
+
+        init_tokens = tokens
+
+        B, _, T, _ = tokens.shape
+
+        if self.add_space_attn:
+            virtual_tokens = self.virual_tracks.repeat(B, 1, T, 1)
+            tokens = torch.cat([tokens, virtual_tokens], dim=1)
+
+        _, N, _, _ = tokens.shape
+
+        j = 0
+        for i in range(len(self.time_blocks)):
+            time_tokens = tokens.contiguous().view(B * N, T, -1)  # B N T C -> (B N) T C
+
+            time_tokens = self.time_blocks[i](time_tokens)
+
+            tokens = time_tokens.view(B, N, T, -1)  # (B N) T C -> B N T C
+            if self.add_space_attn and (i % (len(self.time_blocks) // len(self.space_virtual_blocks)) == 0):
+                space_tokens = tokens.permute(0, 2, 1, 3).contiguous().view(B * T, N, -1)  # B N T C -> (B T) N C
+                point_tokens = space_tokens[:, : N - self.num_virtual_tracks]
+                virtual_tokens = space_tokens[:, N - self.num_virtual_tracks :]
+
+                virtual_tokens = self.space_virtual2point_blocks[j](virtual_tokens, point_tokens, mask=mask)
+                virtual_tokens = self.space_virtual_blocks[j](virtual_tokens)
+                point_tokens = self.space_point2virtual_blocks[j](point_tokens, virtual_tokens, mask=mask)
+
+                space_tokens = torch.cat([point_tokens, virtual_tokens], dim=1)
+                tokens = space_tokens.view(B, T, N, -1).permute(0, 2, 1, 3)  # (B T) N C -> B N T C
+                j += 1
+
+        if self.add_space_attn:
+            tokens = tokens[:, : N - self.num_virtual_tracks]
+
+        tokens = tokens + init_tokens
+
+        # Apply output LayerNorm before final projection
+        tokens = self.output_norm(tokens)
+        flow = self.flow_head(tokens)
+
+        return flow, None
+
+
+class CorrBlock:
+    def __init__(self, fmaps, num_levels=4, radius=4, multiple_track_feats=False, padding_mode="zeros"):
+        """
+        Build a pyramid of feature maps from the input.
+
+        fmaps: Tensor (B, S, C, H, W)
+        num_levels: number of pyramid levels (each downsampled by factor 2)
+        radius: search radius for sampling correlation
+        multiple_track_feats: if True, split the target features per pyramid level
+        padding_mode: passed to grid_sample / bilinear_sampler
+        """
+        B, S, C, H, W = fmaps.shape
+        self.S, self.C, self.H, self.W = S, C, H, W
+        self.num_levels = num_levels
+        self.radius = radius
+        self.padding_mode = padding_mode
+        self.multiple_track_feats = multiple_track_feats
+
+        # Build pyramid: each level is half the spatial resolution of the previous
+        self.fmaps_pyramid = [fmaps]  # level 0 is full resolution
+        current_fmaps = fmaps
+        for i in range(num_levels - 1):
+            B, S, C, H, W = current_fmaps.shape
+            # Merge batch & sequence dimensions
+            current_fmaps = current_fmaps.reshape(B * S, C, H, W)
+            # Avg pool down by factor 2
+            current_fmaps = F.avg_pool2d(current_fmaps, kernel_size=2, stride=2)
+            _, _, H_new, W_new = current_fmaps.shape
+            current_fmaps = current_fmaps.reshape(B, S, C, H_new, W_new)
+            self.fmaps_pyramid.append(current_fmaps)
+
+        # Precompute a delta grid (of shape (2r+1, 2r+1, 2)) for sampling.
+        # This grid is added to the (scaled) coordinate centroids.
+        r = self.radius
+        dx = torch.linspace(-r, r, 2 * r + 1, device=fmaps.device, dtype=fmaps.dtype)
+        dy = torch.linspace(-r, r, 2 * r + 1, device=fmaps.device, dtype=fmaps.dtype)
+        # delta: for every (dy,dx) displacement (i.e. Δx, Δy)
+        self.delta = torch.stack(torch.meshgrid(dy, dx, indexing="ij"), dim=-1)  # shape: (2r+1, 2r+1, 2)
+
+    def corr_sample(self, targets, coords):
+        """
+        Instead of storing the entire correlation pyramid, we compute each level's correlation
+        volume, sample it immediately, then discard it. This saves GPU memory.
+
+        Args:
+          targets: Tensor (B, S, N, C) — features for the current targets.
+          coords: Tensor (B, S, N, 2) — coordinates at full resolution.
+
+        Returns:
+          Tensor (B, S, N, L) where L = num_levels * (2*radius+1)**2 (concatenated sampled correlations)
+        """
+        B, S, N, C = targets.shape
+
+        # If you have multiple track features, split them per level.
+        if self.multiple_track_feats:
+            targets_split = torch.split(targets, C // self.num_levels, dim=-1)
+
+        out_pyramid = []
+        for i, fmaps in enumerate(self.fmaps_pyramid):
+            # Get current spatial resolution H, W for this pyramid level.
+            B, S, C, H, W = fmaps.shape
+            # Reshape feature maps for correlation computation:
+            # fmap2s: (B, S, C, H*W)
+            fmap2s = fmaps.view(B, S, C, H * W)
+            # Choose appropriate target features.
+            fmap1 = targets_split[i] if self.multiple_track_feats else targets  # shape: (B, S, N, C)
+
+            # Compute correlation directly
+            corrs = compute_corr_level(fmap1, fmap2s, C)
+            corrs = corrs.view(B, S, N, H, W)
+
+            # Prepare sampling grid:
+            # Scale down the coordinates for the current level.
+            centroid_lvl = coords.reshape(B * S * N, 1, 1, 2) / (2**i)
+            # Make sure our precomputed delta grid is on the same device/dtype.
+            delta_lvl = self.delta.to(coords.device).to(coords.dtype)
+            # Now the grid for grid_sample is:
+            # coords_lvl = centroid_lvl + delta_lvl   (broadcasted over grid)
+            coords_lvl = centroid_lvl + delta_lvl.view(1, 2 * self.radius + 1, 2 * self.radius + 1, 2)
+
+            # Sample from the correlation volume using bilinear interpolation.
+            # We reshape corrs to (B * S * N, 1, H, W) so grid_sample acts over each target.
+            corrs_sampled = bilinear_sampler(
+                corrs.reshape(B * S * N, 1, H, W), coords_lvl, padding_mode=self.padding_mode
+            )
+            # The sampled output is (B * S * N, 1, 2r+1, 2r+1). Flatten the last two dims.
+            corrs_sampled = corrs_sampled.view(B, S, N, -1)  # Now shape: (B, S, N, (2r+1)^2)
+            out_pyramid.append(corrs_sampled)
+
+        # Concatenate all levels along the last dimension.
+        out = torch.cat(out_pyramid, dim=-1).contiguous()
+        return out
+
+
+def compute_corr_level(fmap1, fmap2s, C):
+    # fmap1: (B, S, N, C)
+    # fmap2s: (B, S, C, H*W)
+    corrs = torch.matmul(fmap1, fmap2s)  # (B, S, N, H*W)
+    corrs = corrs.view(fmap1.shape[0], fmap1.shape[1], fmap1.shape[2], -1)  # (B, S, N, H*W)
+    return corrs / math.sqrt(C)

vggt/layers/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .attention import MemEffAttention

vggt/layers/attention.py

@@ -0,0 +1,98 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+import os
+import warnings
+
+from torch import Tensor
+from torch import nn
+import torch.nn.functional as F
+
+XFORMERS_AVAILABLE = False
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        norm_layer: nn.Module = nn.LayerNorm,
+        qk_norm: bool = False,
+        fused_attn: bool = True,  # use F.scaled_dot_product_attention or not
+        rope=None,
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        self.fused_attn = fused_attn
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.rope = rope
+
+    def forward(self, x: Tensor, pos=None) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if self.rope is not None:
+            q = self.rope(q, pos)
+            k = self.rope(k, pos)
+
+        if self.fused_attn:
+            x = F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                dropout_p=self.attn_drop.p if self.training else 0.0,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None, pos=None) -> Tensor:
+        assert pos is None
+        if not XFORMERS_AVAILABLE:
+            if attn_bias is not None:
+                raise AssertionError("xFormers is required for using nested tensors")
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x

vggt/layers/drop_path.py

@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)

vggt/layers/layer_scale.py

@@ -0,0 +1,27 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma

vggt/layers/patch_embed.py

@@ -0,0 +1,88 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops

vggt/layers/swiglu_ffn.py

@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import os
+from typing import Callable, Optional
+import warnings
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
+# try:
+#     if XFORMERS_ENABLED:
+#         from xformers.ops import SwiGLU
+
+#         XFORMERS_AVAILABLE = True
+#         warnings.warn("xFormers is available (SwiGLU)")
+#     else:
+#         warnings.warn("xFormers is disabled (SwiGLU)")
+#         raise ImportError
+# except ImportError:
+SwiGLU = SwiGLUFFN
+XFORMERS_AVAILABLE = False
+
+# warnings.warn("xFormers is not available (SwiGLU)")
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )

vggt/models/aggregator.py

@@ -0,0 +1,331 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, Union, List, Dict, Any
+
+from vggt.layers import PatchEmbed
+from vggt.layers.block import Block
+from vggt.layers.rope import RotaryPositionEmbedding2D, PositionGetter
+from vggt.layers.vision_transformer import vit_small, vit_base, vit_large, vit_giant2
+
+logger = logging.getLogger(__name__)
+
+_RESNET_MEAN = [0.485, 0.456, 0.406]
+_RESNET_STD = [0.229, 0.224, 0.225]
+
+
+class Aggregator(nn.Module):
+    """
+    The Aggregator applies alternating-attention over input frames,
+    as described in VGGT: Visual Geometry Grounded Transformer.
+
+
+    Args:
+        img_size (int): Image size in pixels.
+        patch_size (int): Size of each patch for PatchEmbed.
+        embed_dim (int): Dimension of the token embeddings.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        mlp_ratio (float): Ratio of MLP hidden dim to embedding dim.
+        num_register_tokens (int): Number of register tokens.
+        block_fn (nn.Module): The block type used for attention (Block by default).
+        qkv_bias (bool): Whether to include bias in QKV projections.
+        proj_bias (bool): Whether to include bias in the output projection.
+        ffn_bias (bool): Whether to include bias in MLP layers.
+        patch_embed (str): Type of patch embed. e.g., "conv" or "dinov2_vitl14_reg".
+        aa_order (list[str]): The order of alternating attention, e.g. ["frame", "global"].
+        aa_block_size (int): How many blocks to group under each attention type before switching. If not necessary, set to 1.
+        qk_norm (bool): Whether to apply QK normalization.
+        rope_freq (int): Base frequency for rotary embedding. -1 to disable.
+        init_values (float): Init scale for layer scale.
+    """
+
+    def __init__(
+        self,
+        img_size=518,
+        patch_size=14,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4.0,
+        num_register_tokens=4,
+        block_fn=Block,
+        qkv_bias=True,
+        proj_bias=True,
+        ffn_bias=True,
+        patch_embed="dinov2_vitl14_reg",
+        aa_order=["frame", "global"],
+        aa_block_size=1,
+        qk_norm=True,
+        rope_freq=100,
+        init_values=0.01,
+    ):
+        super().__init__()
+
+        self.__build_patch_embed__(patch_embed, img_size, patch_size, num_register_tokens, embed_dim=embed_dim)
+
+        # Initialize rotary position embedding if frequency > 0
+        self.rope = RotaryPositionEmbedding2D(frequency=rope_freq) if rope_freq > 0 else None
+        self.position_getter = PositionGetter() if self.rope is not None else None
+
+        self.frame_blocks = nn.ModuleList(
+            [
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    proj_bias=proj_bias,
+                    ffn_bias=ffn_bias,
+                    init_values=init_values,
+                    qk_norm=qk_norm,
+                    rope=self.rope,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+        self.global_blocks = nn.ModuleList(
+            [
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    proj_bias=proj_bias,
+                    ffn_bias=ffn_bias,
+                    init_values=init_values,
+                    qk_norm=qk_norm,
+                    rope=self.rope,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+        self.depth = depth
+        self.aa_order = aa_order
+        self.patch_size = patch_size
+        self.aa_block_size = aa_block_size
+
+        # Validate that depth is divisible by aa_block_size
+        if self.depth % self.aa_block_size != 0:
+            raise ValueError(f"depth ({depth}) must be divisible by aa_block_size ({aa_block_size})")
+
+        self.aa_block_num = self.depth // self.aa_block_size
+
+        # Note: We have two camera tokens, one for the first frame and one for the rest
+        # The same applies for register tokens
+        self.camera_token = nn.Parameter(torch.randn(1, 2, 1, embed_dim))
+        self.register_token = nn.Parameter(torch.randn(1, 2, num_register_tokens, embed_dim))
+
+        # The patch tokens start after the camera and register tokens
+        self.patch_start_idx = 1 + num_register_tokens
+
+        # Initialize parameters with small values
+        nn.init.normal_(self.camera_token, std=1e-6)
+        nn.init.normal_(self.register_token, std=1e-6)
+
+        # Register normalization constants as buffers
+        for name, value in (
+            ("_resnet_mean", _RESNET_MEAN),
+            ("_resnet_std", _RESNET_STD),
+        ):
+            self.register_buffer(
+                name,
+                torch.FloatTensor(value).view(1, 1, 3, 1, 1),
+                persistent=False,
+            )
+
+    def __build_patch_embed__(
+        self,
+        patch_embed,
+        img_size,
+        patch_size,
+        num_register_tokens,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        block_chunks=0,
+        init_values=1.0,
+        embed_dim=1024,
+    ):
+        """
+        Build the patch embed layer. If 'conv', we use a
+        simple PatchEmbed conv layer. Otherwise, we use a vision transformer.
+        """
+
+        if "conv" in patch_embed:
+            self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=3, embed_dim=embed_dim)
+        else:
+            vit_models = {
+                "dinov2_vitl14_reg": vit_large,
+                "dinov2_vitb14_reg": vit_base,
+                "dinov2_vits14_reg": vit_small,
+                "dinov2_vitg2_reg": vit_giant2,
+            }
+
+            self.patch_embed = vit_models[patch_embed](
+                img_size=img_size,
+                patch_size=patch_size,
+                num_register_tokens=num_register_tokens,
+                interpolate_antialias=interpolate_antialias,
+                interpolate_offset=interpolate_offset,
+                block_chunks=block_chunks,
+                init_values=init_values,
+            )
+
+            # Disable gradient updates for mask token
+            if hasattr(self.patch_embed, "mask_token"):
+                self.patch_embed.mask_token.requires_grad_(False)
+
+    def forward(
+        self,
+        images: torch.Tensor,
+    ) -> Tuple[List[torch.Tensor], int]:
+        """
+        Args:
+            images (torch.Tensor): Input images with shape [B, S, 3, H, W], in range [0, 1].
+                B: batch size, S: sequence length, 3: RGB channels, H: height, W: width
+
+        Returns:
+            (list[torch.Tensor], int):
+                The list of outputs from the attention blocks,
+                and the patch_start_idx indicating where patch tokens begin.
+        """
+        B, S, C_in, H, W = images.shape
+
+        if C_in != 3:
+            raise ValueError(f"Expected 3 input channels, got {C_in}")
+
+        # Normalize images and reshape for patch embed
+        images = (images - self._resnet_mean) / self._resnet_std
+
+        # Reshape to [B*S, C, H, W] for patch embedding
+        images = images.view(B * S, C_in, H, W)
+        patch_tokens = self.patch_embed(images)
+
+        if isinstance(patch_tokens, dict):
+            patch_tokens = patch_tokens["x_norm_patchtokens"]
+
+        _, P, C = patch_tokens.shape
+
+        # Expand camera and register tokens to match batch size and sequence length
+        camera_token = slice_expand_and_flatten(self.camera_token, B, S)
+        register_token = slice_expand_and_flatten(self.register_token, B, S)
+
+        # Concatenate special tokens with patch tokens
+        tokens = torch.cat([camera_token, register_token, patch_tokens], dim=1)
+
+        pos = None
+        if self.rope is not None:
+            pos = self.position_getter(B * S, H // self.patch_size, W // self.patch_size, device=images.device)
+
+        if self.patch_start_idx > 0:
+            # do not use position embedding for special tokens (camera and register tokens)
+            # so set pos to 0 for the special tokens
+            pos = pos + 1
+            pos_special = torch.zeros(B * S, self.patch_start_idx, 2).to(images.device).to(pos.dtype)
+            pos = torch.cat([pos_special, pos], dim=1)
+
+        # update P because we added special tokens
+        _, P, C = tokens.shape
+
+        frame_idx = 0
+        global_idx = 0
+        output_list = []
+
+        for _ in range(self.aa_block_num):
+            for attn_type in self.aa_order:
+                if attn_type == "frame":
+                    tokens, frame_idx, frame_intermediates = self._process_frame_attention(
+                        tokens, B, S, P, C, frame_idx, pos=pos
+                    )
+                elif attn_type == "global":
+                    tokens, global_idx, global_intermediates = self._process_global_attention(
+                        tokens, B, S, P, C, global_idx, pos=pos
+                    )
+                else:
+                    raise ValueError(f"Unknown attention type: {attn_type}")
+
+            for i in range(len(frame_intermediates)):
+                # concat frame and global intermediates, [B x S x P x 2C]
+                concat_inter = torch.cat([frame_intermediates[i], global_intermediates[i]], dim=-1)
+                output_list.append(concat_inter)
+
+        del concat_inter
+        del frame_intermediates
+        del global_intermediates
+        return output_list, self.patch_start_idx
+
+    def _process_frame_attention(self, tokens, B, S, P, C, frame_idx, pos=None):
+        """
+        Process frame attention blocks. We keep tokens in shape (B*S, P, C).
+        """
+        # If needed, reshape tokens or positions:
+        if tokens.shape != (B * S, P, C):
+            tokens = tokens.view(B, S, P, C).view(B * S, P, C)
+
+        if pos is not None and pos.shape != (B * S, P, 2):
+            pos = pos.view(B, S, P, 2).view(B * S, P, 2)
+
+        intermediates = []
+
+        # by default, self.aa_block_size=1, which processes one block at a time
+        for _ in range(self.aa_block_size):
+            tokens = self.frame_blocks[frame_idx](tokens, pos=pos)
+            frame_idx += 1
+            intermediates.append(tokens.view(B, S, P, C))
+
+        return tokens, frame_idx, intermediates
+
+    def _process_global_attention(self, tokens, B, S, P, C, global_idx, pos=None):
+        """
+        Process global attention blocks. We keep tokens in shape (B, S*P, C).
+        """
+        if tokens.shape != (B, S * P, C):
+            tokens = tokens.view(B, S, P, C).view(B, S * P, C)
+
+        if pos is not None and pos.shape != (B, S * P, 2):
+            pos = pos.view(B, S, P, 2).view(B, S * P, 2)
+
+        intermediates = []
+
+        # by default, self.aa_block_size=1, which processes one block at a time
+        for _ in range(self.aa_block_size):
+            tokens = self.global_blocks[global_idx](tokens, pos=pos)
+            global_idx += 1
+            intermediates.append(tokens.view(B, S, P, C))
+
+        return tokens, global_idx, intermediates
+
+
+def slice_expand_and_flatten(token_tensor, B, S):
+    """
+    Processes specialized tokens with shape (1, 2, X, C) for multi-frame processing:
+    1) Uses the first position (index=0) for the first frame only
+    2) Uses the second position (index=1) for all remaining frames (S-1 frames)
+    3) Expands both to match batch size B
+    4) Concatenates to form (B, S, X, C) where each sequence has 1 first-position token
+       followed by (S-1) second-position tokens
+    5) Flattens to (B*S, X, C) for processing
+
+    Returns:
+        torch.Tensor: Processed tokens with shape (B*S, X, C)
+    """
+
+    # Slice out the "query" tokens => shape (1, 1, ...)
+    query = token_tensor[:, 0:1, ...].expand(B, 1, *token_tensor.shape[2:])
+    # Slice out the "other" tokens => shape (1, S-1, ...)
+    others = token_tensor[:, 1:, ...].expand(B, S - 1, *token_tensor.shape[2:])
+    # Concatenate => shape (B, S, ...)
+    combined = torch.cat([query, others], dim=1)
+
+    # Finally flatten => shape (B*S, ...)
+    combined = combined.view(B * S, *combined.shape[2:])
+    return combined

vggt/models/vggt.py

@@ -0,0 +1,96 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin  # used for model hub
+
+from vggt.models.aggregator import Aggregator
+from vggt.heads.camera_head import CameraHead
+from vggt.heads.dpt_head import DPTHead
+from vggt.heads.track_head import TrackHead
+
+
+class VGGT(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, img_size=518, patch_size=14, embed_dim=1024):
+        super().__init__()
+
+        self.aggregator = Aggregator(img_size=img_size, patch_size=patch_size, embed_dim=embed_dim)
+        self.camera_head = CameraHead(dim_in=2 * embed_dim)
+        self.point_head = DPTHead(dim_in=2 * embed_dim, output_dim=4, activation="inv_log", conf_activation="expp1")
+        self.depth_head = DPTHead(dim_in=2 * embed_dim, output_dim=2, activation="exp", conf_activation="expp1")
+        self.track_head = TrackHead(dim_in=2 * embed_dim, patch_size=patch_size)
+
+    def forward(
+        self,
+        images: torch.Tensor,
+        query_points: torch.Tensor = None,
+    ):
+        """
+        Forward pass of the VGGT model.
+
+        Args:
+            images (torch.Tensor): Input images with shape [S, 3, H, W] or [B, S, 3, H, W], in range [0, 1].
+                B: batch size, S: sequence length, 3: RGB channels, H: height, W: width
+            query_points (torch.Tensor, optional): Query points for tracking, in pixel coordinates.
+                Shape: [N, 2] or [B, N, 2], where N is the number of query points.
+                Default: None
+
+        Returns:
+            dict: A dictionary containing the following predictions:
+                - pose_enc (torch.Tensor): Camera pose encoding with shape [B, S, 9] (from the last iteration)
+                - depth (torch.Tensor): Predicted depth maps with shape [B, S, H, W, 1]
+                - depth_conf (torch.Tensor): Confidence scores for depth predictions with shape [B, S, H, W]
+                - world_points (torch.Tensor): 3D world coordinates for each pixel with shape [B, S, H, W, 3]
+                - world_points_conf (torch.Tensor): Confidence scores for world points with shape [B, S, H, W]
+                - images (torch.Tensor): Original input images, preserved for visualization
+
+                If query_points is provided, also includes:
+                - track (torch.Tensor): Point tracks with shape [B, S, N, 2] (from the last iteration), in pixel coordinates
+                - vis (torch.Tensor): Visibility scores for tracked points with shape [B, S, N]
+                - conf (torch.Tensor): Confidence scores for tracked points with shape [B, S, N]
+        """
+
+        # If without batch dimension, add it
+        if len(images.shape) == 4:
+            images = images.unsqueeze(0)
+        if query_points is not None and len(query_points.shape) == 2:
+            query_points = query_points.unsqueeze(0)
+
+        aggregated_tokens_list, patch_start_idx = self.aggregator(images)
+
+        predictions = {}
+
+        with torch.cuda.amp.autocast(enabled=False):
+            if self.camera_head is not None:
+                pose_enc_list = self.camera_head(aggregated_tokens_list)
+                predictions["pose_enc"] = pose_enc_list[-1]  # pose encoding of the last iteration
+
+            if self.depth_head is not None:
+                depth, depth_conf = self.depth_head(
+                    aggregated_tokens_list, images=images, patch_start_idx=patch_start_idx
+                )
+                predictions["depth"] = depth
+                predictions["depth_conf"] = depth_conf
+
+            if self.point_head is not None:
+                pts3d, pts3d_conf = self.point_head(
+                    aggregated_tokens_list, images=images, patch_start_idx=patch_start_idx
+                )
+                predictions["world_points"] = pts3d
+                predictions["world_points_conf"] = pts3d_conf
+
+        if self.track_head is not None and query_points is not None:
+            track_list, vis, conf = self.track_head(
+                aggregated_tokens_list, images=images, patch_start_idx=patch_start_idx, query_points=query_points
+            )
+            predictions["track"] = track_list[-1]  # track of the last iteration
+            predictions["vis"] = vis
+            predictions["conf"] = conf
+
+        predictions["images"] = images
+
+        return predictions

vggt/utils/rotation.py

@@ -0,0 +1,138 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Modified from PyTorch3D, https://github.com/facebookresearch/pytorch3d
+
+import torch
+import numpy as np
+import torch.nn.functional as F
+
+
+def quat_to_mat(quaternions: torch.Tensor) -> torch.Tensor:
+    """
+    Quaternion Order: XYZW or say ijkr, scalar-last
+
+    Convert rotations given as quaternions to rotation matrices.
+    Args:
+        quaternions: quaternions with real part last,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    i, j, k, r = torch.unbind(quaternions, -1)
+    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def mat_to_quat(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as rotation matrices to quaternions.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+
+    Returns:
+        quaternions with real part last, as tensor of shape (..., 4).
+        Quaternion Order: XYZW or say ijkr, scalar-last
+    """
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
+
+    batch_dim = matrix.shape[:-2]
+    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(matrix.reshape(batch_dim + (9,)), dim=-1)
+
+    q_abs = _sqrt_positive_part(
+        torch.stack(
+            [
+                1.0 + m00 + m11 + m22,
+                1.0 + m00 - m11 - m22,
+                1.0 - m00 + m11 - m22,
+                1.0 - m00 - m11 + m22,
+            ],
+            dim=-1,
+        )
+    )
+
+    # we produce the desired quaternion multiplied by each of r, i, j, k
+    quat_by_rijk = torch.stack(
+        [
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
+        ],
+        dim=-2,
+    )
+
+    # We floor here at 0.1 but the exact level is not important; if q_abs is small,
+    # the candidate won't be picked.
+    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
+
+    # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
+    # forall i; we pick the best-conditioned one (with the largest denominator)
+    out = quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape(batch_dim + (4,))
+
+    # Convert from rijk to ijkr
+    out = out[..., [1, 2, 3, 0]]
+
+    out = standardize_quaternion(out)
+
+    return out
+
+
+def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
+    """
+    Returns torch.sqrt(torch.max(0, x))
+    but with a zero subgradient where x is 0.
+    """
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    if torch.is_grad_enabled():
+        ret[positive_mask] = torch.sqrt(x[positive_mask])
+    else:
+        ret = torch.where(positive_mask, torch.sqrt(x), ret)
+    return ret
+
+
+def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
+    """
+    Convert a unit quaternion to a standard form: one in which the real
+    part is non negative.
+
+    Args:
+        quaternions: Quaternions with real part last,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Standardized quaternions as tensor of shape (..., 4).
+    """
+    return torch.where(quaternions[..., 3:4] < 0, -quaternions, quaternions)

wandb/debug-internal.log

@@ -1,14 +1,14 @@
-{"time":"2025-07-11T03:30:24.87694446+08:00","level":"INFO","msg":"stream: starting","core version":"0.19.8","symlink path":"/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/vla_projects/simvla_twin2/wandb/offline-run-20250711_033024-ay8ivmb3/logs/debug-core.log"}
-{"time":"2025-07-11T03:30:25.198915791+08:00","level":"INFO","msg":"created new stream","id":"ay8ivmb3"}
-{"time":"2025-07-11T03:30:25.199003577+08:00","level":"INFO","msg":"stream: started","id":"ay8ivmb3"}
-{"time":"2025-07-11T03:30:25.19909892+08:00","level":"INFO","msg":"writer: Do: started","stream_id":"ay8ivmb3"}
-{"time":"2025-07-11T03:30:25.199159482+08:00","level":"INFO","msg":"sender: started","stream_id":"ay8ivmb3"}
-{"time":"2025-07-11T03:30:25.199168784+08:00","level":"INFO","msg":"handler: started","stream_id":"ay8ivmb3"}
-{"time":"2025-07-11T03:30:25.204743769+08:00","level":"INFO","msg":"Starting system monitor"}
-{"time":"2025-07-11T13:57:41.875308993+08:00","level":"INFO","msg":"stream: closing","id":"ay8ivmb3"}
-{"time":"2025-07-11T13:57:41.875434402+08:00","level":"INFO","msg":"Stopping system monitor"}
-{"time":"2025-07-11T13:57:41.935042888+08:00","level":"INFO","msg":"Stopped system monitor"}
-{"time":"2025-07-11T13:57:41.935225363+08:00","level":"INFO","msg":"handler: closed","stream_id":"ay8ivmb3"}
-{"time":"2025-07-11T13:57:41.935234947+08:00","level":"INFO","msg":"writer: Close: closed","stream_id":"ay8ivmb3"}
-{"time":"2025-07-11T13:57:41.935246585+08:00","level":"INFO","msg":"sender: closed","stream_id":"ay8ivmb3"}
-{"time":"2025-07-11T13:57:41.935423591+08:00","level":"INFO","msg":"stream: closed","id":"ay8ivmb3"}
+{"time":"2025-07-13T00:27:14.984102081+08:00","level":"INFO","msg":"stream: starting","core version":"0.19.8","symlink path":"/inspire/hdd/ws-f4d69b29-e0a5-44e6-bd92-acf4de9990f0/public-project/chengdongzhou-240108390137/vla_projects/simvla_twin2/wandb/offline-run-20250713_002714-8tkvnai0/logs/debug-core.log"}
+{"time":"2025-07-13T00:27:15.397571711+08:00","level":"INFO","msg":"created new stream","id":"8tkvnai0"}
+{"time":"2025-07-13T00:27:15.397653899+08:00","level":"INFO","msg":"stream: started","id":"8tkvnai0"}
+{"time":"2025-07-13T00:27:15.397670995+08:00","level":"INFO","msg":"handler: started","stream_id":"8tkvnai0"}
+{"time":"2025-07-13T00:27:15.397696319+08:00","level":"INFO","msg":"writer: Do: started","stream_id":"8tkvnai0"}
+{"time":"2025-07-13T00:27:15.397711661+08:00","level":"INFO","msg":"sender: started","stream_id":"8tkvnai0"}
+{"time":"2025-07-13T00:27:15.4024247+08:00","level":"INFO","msg":"Starting system monitor"}
+{"time":"2025-07-13T00:58:10.192364222+08:00","level":"INFO","msg":"stream: closing","id":"8tkvnai0"}
+{"time":"2025-07-13T00:58:10.192440745+08:00","level":"INFO","msg":"Stopping system monitor"}
+{"time":"2025-07-13T00:58:10.193245449+08:00","level":"INFO","msg":"Stopped system monitor"}
+{"time":"2025-07-13T00:58:10.193432887+08:00","level":"INFO","msg":"handler: closed","stream_id":"8tkvnai0"}
+{"time":"2025-07-13T00:58:10.19344584+08:00","level":"INFO","msg":"sender: closed","stream_id":"8tkvnai0"}
+{"time":"2025-07-13T00:58:10.193443652+08:00","level":"INFO","msg":"writer: Close: closed","stream_id":"8tkvnai0"}
+{"time":"2025-07-13T00:58:10.194622057+08:00","level":"INFO","msg":"stream: closed","id":"8tkvnai0"}

wandb/offline-run-20250711_182438-jxtr69sw/files/requirements.txt

@@ -0,0 +1,199 @@
+openvla-oft==0.0.1
+coloredlogs==15.0.1
+openvla-oft==0.0.1
+pydantic_core==2.27.2
+opt_einsum==3.4.0
+nvidia-cublas-cu12==12.1.3.1
+nltk==3.9.1
+anyio==4.9.0
+tensorflow-addons==0.23.0
+h11==0.14.0
+setproctitle==1.3.5
+json-numpy==2.1.0
+gast==0.6.0
+protobuf==3.20.3
+array_record==0.7.1
+keras==2.15.0
+scipy==1.15.2
+sentencepiece==0.1.99
+Jinja2==3.1.6
+glfw==2.8.0
+tensorflow-io-gcs-filesystem==0.37.1
+transformers==4.40.1
+gitdb==4.0.12
+packaging==24.2
+ml-dtypes==0.2.0
+pillow==11.1.0
+nvidia-cusolver-cu12==11.4.5.107
+jsonlines==4.0.0
+google-auth==2.38.0
+rpds-py==0.23.1
+nvidia-nvjitlink-cu12==12.4.127
+torch==2.2.0
+fonttools==4.56.0
+opencv-python==4.11.0.86
+numba==0.61.0
+jupyter_core==5.7.2
+grpcio==1.71.0
+peft==0.11.1
+annotated-types==0.7.0
+typing-inspect==0.9.0
+termcolor==2.5.0
+antlr4-python3-runtime==4.9.3
+markdown-it-py==3.0.0
+huggingface-hub==0.29.3
+imageio==2.37.0
+nvidia-nvtx-cu12==12.1.105
+draccus==0.8.0
+mypy-extensions==1.0.0
+future==1.0.0
+onnxsim==0.4.36
+tensorboard-data-server==0.7.2
+six==1.17.0
+tqdm==4.67.1
+rsa==4.9
+typing_extensions==4.12.2
+rich==13.9.4
+nvidia-cusparse-cu12==12.1.0.106
+jsonschema-specifications==2024.10.1
+libclang==18.1.1
+ninja==1.11.1.3
+cloudpickle==3.1.1
+onnx==1.17.0
+python-xlib==0.33
+referencing==0.36.2
+filelock==3.18.0
+debugpy==1.8.13
+pip==25.0
+mdurl==0.1.2
+tensorflow-graphics==2021.12.3
+pydantic==2.10.6
+docker-pycreds==0.4.0
+kiwisolver==1.4.8
+networkx==3.4.2
+pyasn1==0.6.1
+humanfriendly==10.0
+pynput==1.8.0
+certifi==2025.1.31
+pytest==8.3.5
+sniffio==1.3.1
+nbformat==5.10.4
+requests-oauthlib==2.0.0
+etils==1.12.2
+tensorflow-estimator==2.15.0
+cachetools==5.5.2
+click==8.1.8
+importlib_resources==6.5.2
+robosuite==1.4.1
+pyasn1_modules==0.4.1
+nvidia-nccl-cu12==2.19.3
+qwen-vl-utils==0.0.11
+cycler==0.12.1
+nvidia-cufft-cu12==11.0.2.54
+typeguard==2.13.3
+iniconfig==2.0.0
+idna==3.10
+MarkupSafe==3.0.2
+matplotlib==3.10.1
+promise==2.3
+easydict==1.13
+tensorflow-datasets==4.9.3
+Werkzeug==3.1.3
+tomli==2.2.1
+nvidia-cuda-cupti-cu12==12.1.105
+omegaconf==2.3.0
+imageio-ffmpeg==0.6.0
+absl-py==2.1.0
+mujoco==3.3.0
+evdev==1.9.1
+sentry-sdk==2.22.0
+pyparsing==3.2.1
+dm-tree==0.1.9
+psutil==7.0.0
+torchaudio==2.2.0
+h5py==3.13.0
+PyOpenGL==3.1.9
+triton==2.2.0
+fsspec==2025.3.0
+nvidia-cudnn-cu12==8.9.2.26
+trimesh==4.6.4
+Pygments==2.19.1
+nvidia-cuda-runtime-cu12==12.1.105
+wheel==0.45.1
+astunparse==1.6.3
+requests==2.32.3
+importlib_metadata==8.6.1
+starlette==0.46.1
+charset-normalizer==3.4.1
+tokenizers==0.19.1
+accelerate==1.5.2
+tensorflow-metadata==1.16.1
+OpenEXR==3.3.2
+mpmath==1.3.0
+einops==0.8.1
+google-pasta==0.2.0
+exceptiongroup==1.2.2
+bddl==3.5.0
+safetensors==0.5.3
+nvidia-cuda-nvrtc-cu12==12.1.105
+regex==2024.11.6
+zipp==3.21.0
+mdit-py-plugins==0.4.2
+contourpy==1.3.1
+nvidia-cusparselt-cu12==0.6.2
+wandb==0.19.8
+tensorboard==2.15.2
+wrapt==1.14.1
+pyyaml-include==1.4.1
+urllib3==2.3.0
+setuptools==75.8.0
+fastjsonschema==2.21.1
+fastapi==0.115.11
+oauthlib==3.2.2
+uvicorn==0.34.0
+gym-notices==0.0.8
+jupytext==1.16.7
+diffusers==0.32.2
+flatbuffers==25.2.10
+timm==0.9.10
+traitlets==5.14.3
+tensorflow==2.15.0
+flash-attn==2.5.5
+Markdown==3.7
+torchvision==0.17.0
+smmap==5.0.2
+attrs==25.3.0
+google-auth-oauthlib==1.2.1
+av==14.3.0
+onnxruntime==1.21.0
+gym==0.26.2
+platformdirs==4.3.6
+mergedeep==1.3.4
+nvidia-curand-cu12==10.3.2.106
+python-dateutil==2.9.0.post0
+toml==0.10.2
+numpy==1.26.4
+GitPython==3.1.44
+jsonschema==4.23.0
+joblib==1.4.2
+PyYAML==6.0.2
+sympy==1.13.1
+llvmlite==0.44.0
+pluggy==1.5.0
+dlimp==0.0.1
+jaraco.collections==5.1.0
+packaging==24.2
+importlib_metadata==8.0.0
+tomli==2.0.1
+backports.tarfile==1.2.0
+typing_extensions==4.12.2
+jaraco.context==5.3.0
+typeguard==4.3.0
+wheel==0.43.0
+autocommand==2.2.2
+jaraco.text==3.12.1
+more-itertools==10.3.0
+platformdirs==4.2.2
+inflect==7.3.1
+jaraco.functools==4.0.1
+zipp==3.19.2