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+    ],
+    "cameras": [
+      "front_camera",
+      "left_camera"
+    ],
+    "action_indices": "0..2037 inclusive",
+    "proprio_indices": "0..2038 inclusive"
+  }
+]

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/controller.py

@@ -0,0 +1,127 @@
+"""Task-local demo-reference tracking with visible selection and bounded feedback."""
+from pathlib import Path
+import json,numpy as np,cv2
+from visual_features import marker_features,marker_distance
+ROOT=Path(__file__).parent
+CONFIG=json.loads((ROOT/'settings.json').read_text())
+ROBOTS=('panda_leaphand-1','panda_leaphand_left-0')
+_STATE={};_BANK=None
+
+def _load():
+ global _BANK
+ if _BANK is None:
+  with np.load(ROOT/'reference_bank.npz') as f:b={k:f[k] for k in f.files}
+  with np.load(ROOT/'numeric_demos.npz') as f:b['numeric']={k:f[k] for k in f.files}
+  b['hand_map']=np.load(ROOT/'hand_map.npy');b['hand_sign']=np.load(ROOT/'hand_sign.npy')
+  b['table_features']=np.load(ROOT/'table_features.npy')
+  b['markers']=np.load(ROOT/'marker_bank.npy');b['feedback']=np.load(ROOT/'pose_feedback.npy')
+  with np.load(ROOT/'geometry_feedback.npz') as f:b['geometry']={k:f[k] for k in f.files}
+  b['indices']=[np.flatnonzero(b['demo_index']==i) for i in range(20)]
+  b['flip']=[int(np.flatnonzero((np.linalg.norm(b['numeric']['a'+str(i)][:,3:6],axis=1)>.25)&(np.arange(len(b['numeric']['a'+str(i)]))>len(b['numeric']['a'+str(i)])*.65))[0]) for i in range(20)]
+  b['gate']=[int(np.flatnonzero(b['numeric']['a'+str(i)][:400,23]<-.04)[-1]) for i in range(20)]
+  _BANK=b
+ return _BANK
+
+def reset_episode():
+ _STATE.clear();_STATE.update(last_step=0,t=-1.,was_recovering=False,demo=None,gate_done=False,recovery=0,bad=0,reason='reset',phase=0.)
+
+def image_features(rgb):
+ return np.concatenate([cv2.resize(np.asarray(rgb[c]).squeeze(),(16,12),interpolation=cv2.INTER_AREA).reshape(-1).astype(np.float32)/255 for c in ['front_camera','left_camera']])
+
+def scene_features(m):
+ out=[]
+ for st in [8,20]:
+  p=m[st:st+4].reshape(2,2)
+  if np.any(p<0):return None
+  p=p[np.argsort(p[:,1 if st==20 else 0])];out.extend(p.mean(0));out.extend(p[1]-p[0])
+ return np.array(out)
+
+def observe_scene(m,reference,previous):
+ result=np.asarray(previous if previous is not None else reference,np.float32).copy();visible=0
+ for cam,st in enumerate([8,20]):
+  p=m[st:st+4].reshape(2,2);p=p[p[:,0]>=0];visible+=len(p);base=result[cam*4:cam*4+4]
+  if len(p)==2:
+   p=p[np.argsort(p[:,cam])];result[cam*4:cam*4+4]=np.r_[p.mean(0),p[1]-p[0]]
+  elif len(p)==1:
+   endpoints=np.stack([base[:2]-.5*base[2:],base[:2]+.5*base[2:]])
+   side=int(np.argmin(np.sum((endpoints-p[0])**2,axis=1)))
+   result[cam*4:cam*4+2]=p[0]+(.5 if side==0 else -.5)*base[2:]
+ return result,visible
+
+def leg_error(m):
+ red=m[:8].reshape(4,2);blue=m[8:12].reshape(2,2)
+ if np.any(red[:,0]<0) or np.any(blue[:,0]<0):return None
+ upper=red[np.argsort(red[:,1])[:2]]
+ return float(np.min(upper[:,0])-np.min(blue[:,0]))
+
+def act(obs):
+ if not _STATE:reset_episode()
+ step=int(obs['step_id']);elapsed=step-_STATE['last_step']
+ if step>=min(2400,CONFIG.get('stop_step',2400)):return None
+ if elapsed<=0:raise ValueError('Non-increasing step')
+ _STATE['last_step']=step;b=_load();p=obs['proprioception']
+ q=np.concatenate([np.asarray(p['agent/'+r+'/qpos']).reshape(-1) for r in ROBOTS])
+ if q.shape!=(46,) or not np.isfinite(q).all():return None
+ im=image_features(obs['rgb']);marks=marker_features(obs['rgb'])
+ if _STATE['demo'] is None or step<=9:
+  ids=np.array([x[min(int((step-1)//8),len(x)-1)] for x in b['indices']])
+  ds=marker_distance(b['markers'][ids],marks)*CONFIG.get('marker_weight',.1)+np.mean(((b['image'][ids]-im)/.15)**2,1)
+  _STATE['demo']=int(np.argmin(ds));_STATE['selection_distance']=float(ds.min())
+ did=_STATE['demo'];a=b['numeric']['a'+str(did)];qref=b['numeric']['q'+str(did)];n=len(a)
+ if not _STATE['was_recovering']:_STATE['t']+=elapsed*CONFIG.get('speed',1.)
+ t=int(round(_STATE['t']));gate=b['gate'][did];err=leg_error(marks);_STATE['leg_error']=err
+ recovering=False
+ if CONFIG.get('leg_gate',False) and not _STATE['gate_done'] and t>=gate:
+  if err is not None and err<=CONFIG.get('leg_threshold',.025):_STATE['gate_done']=True
+  elif _STATE['recovery']>=CONFIG.get('recovery_limit',80):
+   _STATE['reason']='first-leg recovery exhausted; failed attempt';return None
+  else:
+   recovering=True;_STATE['recovery']+=elapsed;t=gate-20
+ if t>=n:
+  _STATE['reason']='reference endpoint reached; not a success assertion';return None
+ j=b['indices'][did][min(t//8,len(b['indices'][did])-1)]
+ action=a[t].copy();error=qref[t]-q
+ flipping=t>=b['flip'][did]-4
+ _STATE['flipping']=bool(flipping)
+ _STATE['geometry_offset_norm']=0.
+ geometry_joint_offset=np.zeros(14,np.float32)
+ if CONFIG.get('geometry_gain',0.) and t>n*.02 and not flipping:
+  reference=b['table_features'][j]
+  current,visible=observe_scene(marks,reference,_STATE.get('table_geometry'))
+  _STATE['table_geometry']=current.tolist();_STATE['geometry_visible_points']=int(visible)
+  previous=np.asarray(_STATE.get('geometry_offset',[0.]*14),np.float32)
+  offset=previous
+  if current is not None and reference is not None:
+   g=b['geometry'];k=int(np.argmin(abs(g['grid']-g['aligned_phase'][j])))
+   if g['valid'][k]>=8:
+    target=CONFIG['geometry_gain']*((current-reference)@g['jac'][k])
+    target=np.clip(target,-CONFIG.get('geometry_limit',.1),CONFIG.get('geometry_limit',.1))
+    alpha=1.-.9**elapsed;offset=previous+alpha*(target-previous)
+  _STATE['geometry_offset']=offset.tolist()
+  geometry_joint_offset=offset
+  _STATE['geometry_offset_norm']=float(np.linalg.norm(offset))
+ for r in range(2):
+  qs=slice(r*23,r*23+7);aa=slice(r*22,r*22+6)
+  gain=0. if (r==0 and flipping and CONFIG.get('disable_flip_feedback',True)) else CONFIG.get('pose_gain',.1)
+  correction=gain*(error[qs]@b['feedback'][j,r])
+  correction[3:]=0.  # Preserve demonstrated wrist orientation commands.
+  correction[:3]+=gain*(geometry_joint_offset[r*7:r*7+7]@b['feedback'][j,r])[:3]
+  action[aa]+=np.clip(correction,-CONFIG.get('pose_limit',.02),CONFIG.get('pose_limit',.02))
+  hs=slice(r*23+7,r*23+23);ha=slice(r*22+6,r*22+22)
+  action[ha]+=np.clip(CONFIG.get('hand_gain',.1)*error[hs][b['hand_map'][r]]*b['hand_sign'][r]/.1,-CONFIG.get('hand_limit',.05),CONFIG.get('hand_limit',.05))
+ if recovering:
+  action[:22]=0
+  # Continue the demonstrated pull, without pulling back toward an earlier pose.
+  action[22:25]=a[max(gate-30,0),22:25]
+ if recovering:_STATE['t']=float(gate)
+ _STATE['was_recovering']=recovering
+ _STATE['phase']=float(_STATE['t']/n);_STATE['frame']=t
+ _STATE['arm_distance']=float(np.mean((error[:7]/b['q_scale'][:7])**2))
+ support_error=error.copy()
+ if flipping and CONFIG.get('disable_flip_feedback',True):support_error[:7]=0
+ _STATE['distance']=float(np.mean((support_error/b['q_scale'])**2))
+ _STATE['bad']=_STATE['bad']+elapsed if _STATE['distance']>CONFIG.get('pose_abort',3.) else 0
+ if _STATE['bad']>40:
+  _STATE['reason']='sustained pose deviation; failed attempt';return None
+ _STATE['reason']='visible leg recovery' if recovering else 'reference tracking with joint feedback'
+ return np.clip(action,-1,1).astype(np.float32)

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/demo_manifest.json

@@ -0,0 +1,2168 @@
+{
+  "task_id": "DualArmFlipTable-v1",
+  "status": "released",
+  "selection_rule": "prefer existing pinned D20, otherwise numeric trajectory ID sort before reading data",
+  "lineage_status": "pinned_comparator",
+  "episodes": [
+    {
+      "demo_id": "demo_00",
+      "file": "demo_00.h5",
+      "length": 2060,
+      "fields": [
+        {
+          "path": "actions/panda_leaphand-1",
+          "shape": [
+            2060,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda_leaphand_left-0",
+          "shape": [
+            2060,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qpos",
+          "shape": [
+            2061,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            2061,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            2061,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            2061,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            2061,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            2061,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            2061,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            2061,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            2061,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "051c72dd27c17b55ea7a49a68bb4e5ee269f06f1d1fe090db75f0533faa312f7",
+      "source_sha256": "d96d354fea4daf240c4913ce970e93b7b36c7860b83db008177d4a1961207a84",
+      "view_sha256": "0d122c27e5c21ee41769b874cc72e645f46a90a817b85420703a39964b0a415d"
+    },
+    {
+      "demo_id": "demo_01",
+      "file": "demo_01.h5",
+      "length": 1934,
+      "fields": [
+        {
+          "path": "actions/panda_leaphand-1",
+          "shape": [
+            1934,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda_leaphand_left-0",
+          "shape": [
+            1934,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qpos",
+          "shape": [
+            1935,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            1935,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            1935,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            1935,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            1935,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            1935,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            1935,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            1935,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            1935,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "d70c4feb4ae57f5b9ad9f2bbdce4b559650aa13a6f1cca2c87c47590ed617b89",
+      "source_sha256": "2c9a1a3acb6f8ff8b29d787599e9a8f5597db867841d4d9f58e409328c2f6613",
+      "view_sha256": "f49a92feac7173f5b81a41cec5df883352b8941a61564d4fc5320db921ae101f"
+    },
+    {
+      "demo_id": "demo_02",
+      "file": "demo_02.h5",
+      "length": 1950,
+      "fields": [
+        {
+          "path": "actions/panda_leaphand-1",
+          "shape": [
+            1950,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda_leaphand_left-0",
+          "shape": [
+            1950,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qpos",
+          "shape": [
+            1951,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            1951,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            1951,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            1951,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            1951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            1951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            1951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            1951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            1951,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "aea6b5ebe3d71e7d31ff4a07b898cd03fc03d066083113ad243305604758e9f3",
+      "source_sha256": "0ee8ed58bca36dcca5ce39d31dd01fc9a91ba68c6a5cd24e0673f3c132bde85c",
+      "view_sha256": "39703e95414e2c55a27f732fda6568f3f0b25dcd4f426b378acd271543d0717d"
+    },
+    {
+      "demo_id": "demo_03",
+      "file": "demo_03.h5",
+      "length": 1997,
+      "fields": [
+        {
+          "path": "actions/panda_leaphand-1",
+          "shape": [
+            1997,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda_leaphand_left-0",
+          "shape": [
+            1997,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qpos",
+          "shape": [
+            1998,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            1998,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            1998,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            1998,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            1998,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            1998,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            1998,
+            240,
+            320,
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+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            2039,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            2039,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            2039,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            2039,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            2039,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "8bbd1876cae25c6a9dcfec4d0cc7112bcf9f380f23816f3b75159cb5feaa07fb",
+      "source_sha256": "5210b06a6f361f65811d2c86dbf2c9afd2f5a587dc3cd85125bc0c7ab3e8122e",
+      "view_sha256": "9f52b913b3e2179773509f7d660e5e0d06a4791cdf00de9d7920232bfdaed7f7"
+    }
+  ]
+}

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/environment.lock

@@ -0,0 +1,249 @@
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agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/findings.md

@@ -0,0 +1,32 @@
+# Findings and audit
+
+Only this task's 20 authorized demos and charged legal API exploration were used. No neural weights were fitted or changed.
+
+The original blended retrieval stalled at a direction reversal. Single-label retrieval progressed but failed to unfold/flip reliably. Direct hand-qpos feedback was unsafe because action and qpos joint ordering differ. Episode 7 measured the correct permutation on both hands: [4,0,8,12,5,1,9,13,6,2,10,14,3,7,11,15]. Three .08 pulses produced approximately .02447 rad, consistent with native units.
+
+A coherent visually selected reference with joint feedback improved leg manipulation. Full tests exposed table/hand misalignment and large joint-coordinate changes during the flip. Current-image geometry adjustment is a bounded numerical calibration from legal demos, not a neural model. Raw qpos difference is not used to abort the right arm during the large flip rotation when configured to disable flip feedback. Regression-derived arm rotation corrections were removed after direct demo/live grasp comparison; demonstrated wrist commands are preserved, with any configured time scaling shown in settings.
+
+The independent runner rejected a four-action output at step 1 in episode 4. Single 44-vector output and None termination were independently validated in episode 9. Runtime therefore emits one action per call.
+
+## Development evidence
+
+| Episode | Recorded result |
+|---|---|
+| 1 | Interactive: step 599; observation-conditioned reference action; native success seen=None (None means not retained in early harness). |
+| 2 | Independent: finished; native success=False; hash ecd01c65829bbad2f157f08731ce4f1d10183104a3e36da0ddb5021af4024776 |
+| 3 | Interactive: step 397; observation-conditioned reference action; native success seen=None (None means not retained in early harness). |
+| 4 | Independent: exception; native success=False; hash dc9e1a729475c3a5a6fe154acbc8c9274fb300efb4bd17eb6653238e6938ccdb |
+| 5 | Interactive: step 281; outside reference support; native success seen=None (None means not retained in early harness). |
+| 6 | Interactive: step 397; observation-conditioned reference action; native success seen=None (None means not retained in early harness). |
+| 7 | Finger-order pulse calibration; unique mapping on both hands; no task success claim. |
+| 8 | Interactive: step 1873; sustained pose deviation; failed attempt; native success seen=False (None means not retained in early harness). |
+| 9 | Independent: finished; native success=False; hash 63198c6d2651047751520526f7fb458ae8b333fb81dad128b6f1fe0843dae45d |
+| 10 | Interactive: step 1953; reference endpoint reached; not a success assertion; native success seen=False (None means not retained in early harness). |
+| 11 | Interactive: step 797; reference tracking with joint feedback; native success seen=False (None means not retained in early harness). |
+| 12 | Interactive: step 1900; reference endpoint reached; not a success assertion; native success seen=False (None means not retained in early harness). |
+| 13 | Interactive: step 2062; reference endpoint reached; not a success assertion; native success seen=False (None means not retained in early harness). |
+| 14 | Interactive: step 1892; reference endpoint reached; not a success assertion; native success seen=False (None means not retained in early harness). |
+
+See /workspace/exploration/ledger.md, summary.json, version snapshots, native logs, observations, and action arrays for the full audit. Native feedback is authoritative when present. A native score overwrite attempt in episode 2 was rejected; its false score was retained. If native feedback is absent, the preregistered fallback requires unambiguous visible unfolded legs and upright tabletop; ambiguity is false.
+
+Final four exact independent rehearsal results and freeze metadata are preserved outside candidate so the same bundle can be repeated without modification. Reference phase, endpoint termination, pose support, and visible leg metrics are never official success claims.

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/freeze_manifest.json

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+      {
+        "bundle_hash": "ab6098a01f9991f5851a6a351f4b99be27eda39563ed9d20bd5dd4c7a64b480c",
+        "episode": 16,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": false,
+        "valid": true
+      },
+      {
+        "episode": 18,
+        "bundle_hash": "ab6098a01f9991f5851a6a351f4b99be27eda39563ed9d20bd5dd4c7a64b480c",
+        "independent": true,
+        "valid": true,
+        "status": "exception",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 19,
+        "bundle_hash": "ab6098a01f9991f5851a6a351f4b99be27eda39563ed9d20bd5dd4c7a64b480c",
+        "independent": true,
+        "valid": true,
+        "status": "exception",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 20,
+        "bundle_hash": "ab6098a01f9991f5851a6a351f4b99be27eda39563ed9d20bd5dd4c7a64b480c",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      }
+    ]
+  }
+}

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/handoff.md

@@ -0,0 +1,27 @@
+# Handoff
+
+Entry point: controller.py exposes reset_episode() and act(obs). Call reset_episode once per new episode. act reads step_id, front_camera/left_camera uint8 RGB, and both 23-element qpos arrays in the contract paths. It returns one finite float32 vector of length 44 in [-1,1], or None to stop. The runner owns the initial charged hold. Native horizon is 2400 at 20 Hz.
+
+Runtime uses only Python, NumPy, OpenCV, and the adjacent bundled files. No API, network, external model, online fitting, or development agent is needed. All reference arrays come from the authorized 20 demos; the hand mapping comes from legal exploration episode 7. Settings below describe the submitted behavior.
+
+```json
+{
+  "marker_weight": 0.1,
+  "pose_gain": 0.15,
+  "pose_limit": 0.06,
+  "hand_gain": 0.1,
+  "hand_limit": 0.05,
+  "speed": 1.0,
+  "leg_gate": false,
+  "recovery_limit": 80,
+  "leg_threshold": 0.025,
+  "pose_abort": 3.0,
+  "geometry_gain": 1.0,
+  "geometry_limit": 0.3,
+  "disable_flip_feedback": true
+}
+```
+
+The reference endpoint and recovery/pose aborts are not success claims. Both current table appearance and contacts can differ from demonstrations. Linear geometry and arm feedback are approximate; contact and grasp robustness must be assessed by independent rehearsals.
+
+The audit remains in /workspace/demo_analysis.md, /workspace/exploration, and /workspace/versions. The runner supplies the verified demo manifest, readonly demos, and common environment lock. Final exact rehearsal and freeze metadata are stored outside this directory to preserve bundle identity.

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/model_tool_manifest.yaml

@@ -0,0 +1,68 @@
+{
+  "task_id": "DualArmFlipTable-v1",
+  "protocol": "Agent-D20-E20-Compiled",
+  "runtime_dependencies": [
+    "Python standard library",
+    "numpy",
+    "cv2"
+  ],
+  "runtime_network": false,
+  "runtime_environment_imports": false,
+  "neural_weights_modified": false,
+  "pretrained_weights": [],
+  "authorized_demo_ids": [
+    "demo_00",
+    "demo_01",
+    "demo_02",
+    "demo_03",
+    "demo_04",
+    "demo_05",
+    "demo_06",
+    "demo_07",
+    "demo_08",
+    "demo_09",
+    "demo_10",
+    "demo_11",
+    "demo_12",
+    "demo_13",
+    "demo_14",
+    "demo_15",
+    "demo_16",
+    "demo_17",
+    "demo_18",
+    "demo_19"
+  ],
+  "legal_exploration_calibration": "Episode 7 hand permutation/sign; other exploration used to select controller settings",
+  "numeric_models": "Nonparametric demo references, local linear arm calibration, and bounded marker-to-joint numerical geometry calibration. No neural training.",
+  "source_sha256": {
+    "/task_contract.json": "5debae4735ab904d6710fed731a31c74e6bb64d260ce8c8813421c9762b764ef",
+    "/protocol_for_worker.json": "7833632cc0cb7b6d4bfad5727ac38cce7d2ceaaac8fabd7c5b4d710dfc5f2c32",
+    "/demo_manifest.json": "022a515f4aa615c072def4b3949b47b3593d89b8e1d4083c7b2310ea84c09539"
+  },
+  "artifact_sha256": {
+    "compilation_indices.json": "0b5517936dcea0b656c2c1794baa28447336ba07b3e35adff5ff885c16e0b33b",
+    "controller.py": "d20bfd35b3d822cbb377a3d4c16a1297ba9e38b218c2dc9322b8c986cea00506",
+    "findings.md": "088084ff14bda99d7bc374b632b87756f161ebeff6c96fc78b1115bef9df66c4",
+    "geometry_feedback.npz": "bb9696f8752e2f4b6c36c29b4a85e26ca2ddd26bd7388e7a27e52c0bfc340689",
+    "hand_map.npy": "535b33f368a4c6e49257cd819c856a868bb6f74d0803a86ca46dccc9ba23884a",
+    "hand_sign.npy": "adfcf05fc49ff553773a66d9bb57399c8feb7a42469bd969e00109aef25b9f39",
+    "handoff.md": "f8a8969fc05b51d96b9ec04df522b13a5bb2cb56eff96ce97b7765cf493192d9",
+    "marker_bank.npy": "340230ceaf9b6608a0785df1a4badf92eb6b6ba2f7b848fd3a8a313d38a8a3b2",
+    "numeric_demos.npz": "cfb747e06fded6acccec96cad9d4e3a7b9dbb67db9ca14d114b8a02a15d4ba6b",
+    "pose_feedback.npy": "1b2a579fab5c22229b18c4bfb7bec9535278ec32bf3552c4919a3bb799699df6",
+    "reference_bank.npz": "0e489a26fd4533863fdf5332be38b9e0dff03847d8665c6887c1e449ea8f5f09",
+    "settings.json": "60b975af0abe317895c00bf5bfe7a430a2b39c2802a81e307bf15ab2213682d6",
+    "skills.md": "e30fc4e3154193e46d7566ced8f0d9a77cf78d36eed133c8f409c6bca7e83a01",
+    "table_features.npy": "6130204a75a93ba29839bf5ea5c5482b072258a0a4d9a6a340311da5b740c960",
+    "validation.txt": "2e86037161253e500db12be4a5a21ab2741b2b715d8e85bb8d99feeeb859e1f4",
+    "visual_features.py": "5a85357cabe89a731f89080d4ce5ce9ba9e8a9954a9ebc4c28de187e4214e2c2"
+  },
+  "python": "3.10.20",
+  "package_versions": {
+    "numpy": "2.2.6",
+    "opencv-python": "4.13.0.92"
+  },
+  "common_environment_lock": "Added by trusted runner",
+  "rehearsal_results": "Stored externally in exploration/rehearsal*.json; exact bundle hashes returned by API",
+  "score_policy": "Use native feedback when present; otherwise require unambiguous visible task completion and score ambiguity false."
+}

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/policy_runner.py

@@ -0,0 +1,25 @@
+"""Frozen-program entry point; no development agent and no reset capability."""
+import importlib.util
+import sys
+
+sys.path.insert(0,'/artifact')
+sys.path.insert(0,'/')
+from worker_api import API
+
+
+def main():
+    spec=importlib.util.spec_from_file_location('controller','/artifact/controller.py')
+    module=importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    module.reset_episode()
+    api=API()
+    result=api.latest_observation()
+    while not (result['terminated'] or result['truncated'] or result['horizon_reached']):
+        obs=dict(result['observation'],step_id=result['step_id'])
+        action=module.act(obs)
+        if action is None:break
+        response=api.execute_actions([action])
+        result=response['steps'][-1]
+
+
+if __name__=='__main__':main()

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/settings.json

@@ -0,0 +1,15 @@
+{
+  "marker_weight": 0.1,
+  "pose_gain": 0.15,
+  "pose_limit": 0.06,
+  "hand_gain": 0.1,
+  "hand_limit": 0.05,
+  "speed": 1.0,
+  "leg_gate": false,
+  "recovery_limit": 80,
+  "leg_threshold": 0.025,
+  "pose_abort": 3.0,
+  "geometry_gain": 1.0,
+  "geometry_limit": 0.3,
+  "disable_flip_feedback": true
+}

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/skills.md

@@ -0,0 +1,11 @@
+# Task-local control procedure
+
+1. Respect the 44-component order: panda_leaphand-1 then panda_leaphand_left-0, each 6 arm plus 16 hand deltas.
+2. The runner supplies the charged hold; step 1 is already consumed. Use only legal RGB/proprioception and step_id.
+3. Select a reference using visible front/left RGB and colored tabletop markers during the initial observations. Track its native-step action sequence with bounded feedback.
+4. Use the measured hand permutation; never equate hand qpos ordering with action ordering.
+5. Preserve demonstrated wrist commands; learned corrections affect arm translation and mapped fingers only. When enabled, smooth and bound current-marker geometry corrections. Missing markers are unknown; they are not completion evidence.
+6. Large flip rotations can change the arm's IK branch. Follow configured flip feedback handling and preserve action bounds.
+7. None ends an attempt and does not assert success. Native feedback or the conservative visible fallback determines scoring.
+
+No external Codex skill, network model, development agent, simulator import, seed access, or state restoration is part of the runtime.

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/validation.txt

@@ -0,0 +1 @@
+Final controller and visual feature syntax compiled successfully. Live diagnostics and independent runner results are listed in findings.md and the external audit. Synthetic interface checks are not task-success evidence. Final four rehearsals must use this exact unchanged directory.

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/visual_features.py

@@ -0,0 +1,23 @@
+import cv2,numpy as np
+def marker_features(rgb):
+ f=[]
+ for c in ['front_camera','left_camera']:
+  im=np.asarray(rgb[c]).squeeze()
+  for mask,count in [((im[:,:,0]>170)&(im[:,:,1]<100)&(im[:,:,2]<100),4),((im[:,:,2]>170)&(im[:,:,0]<100)&(im[:,:,1]<130),2)]:
+   n,l,s,p=cv2.connectedComponentsWithStats(mask.astype(np.uint8))
+   z=sorted([(ss[4],pp) for ss,pp in zip(s[1:],p[1:]) if ss[4]>=3],reverse=True,key=lambda x:x[0])[:count]
+   p=np.array([x[1]/[im.shape[1],im.shape[0]] for x in z]).reshape(-1,2)
+   if len(p):p=p[np.argsort(p[:,0])]
+   a=np.full((count,2),-1.,np.float32);a[:len(p)]=p;f.extend(a.flatten())
+ return np.array(f,np.float32)
+def marker_distance(bank,current):
+ # Color and camera are preserved; point order within a color is irrelevant.
+ ds=np.zeros(len(bank),np.float32)
+ for start,n in [(0,4),(8,2),(12,4),(20,2)]:
+  b=bank[:,start:start+2*n].reshape(-1,n,2);c=current[start:start+2*n].reshape(n,2);validc=c[:,0]>=0;validb=b[:,:,0]>=0
+  if not validc.any():continue
+  dd=np.sum((b[:,:,None,:]-c[None,None,:,:])**2,axis=-1);dd=np.where(validb[:,:,None]&validc[None,None,:],dd,1.)
+  left=np.where(validb,dd.min(2),0).sum(1)/np.maximum(validb.sum(1),1)
+  right=np.where(validc[None,:],dd.min(1),0).sum(1)/validc.sum()
+  ds+=np.minimum((left+right)/2,.02)/(.03**2)
+ return ds/4

agent_d20_e20/frozen_20261102/tasks/DualArmFlipTable-v1/frozen/worker_api.py

@@ -0,0 +1,81 @@
+"""Standalone worker client; safe to mount without the runner package."""
+import base64
+import json
+import socket
+import struct
+import uuid
+import os
+from pathlib import Path
+
+import numpy as np
+
+
+def array_hook(value):
+    if value.get('__array__') is True:
+        dtype=np.dtype(value['dtype'])
+        if dtype.kind not in 'bifu':raise ValueError('non-numeric array from service')
+        raw=base64.b64decode(value['data'],validate=True)
+        return np.frombuffer(raw,dtype=dtype).reshape(value['shape']).copy()
+    return value
+
+
+class API:
+    def __init__(self,socket_path='/rpc/control.sock',demo_root='/demonstrations',manifest='/demo_manifest.json'):
+        self.socket_path=socket_path
+        self.demo_root=Path(demo_root)
+        self.manifest=Path(manifest)
+
+    def call(self,method,arguments=None,request_id=None):
+        # Caller can persist a request ID before sending. Never automatically retry.
+        request_id=request_id or str(uuid.uuid4())
+        request={'request_id':request_id,'method':method,'arguments':arguments or {}}
+        if os.environ.get('AGENT_E20_RPC_TOKEN'):
+            request['token']=os.environ['AGENT_E20_RPC_TOKEN']
+        payload=json.dumps(request,allow_nan=False).encode()
+        if len(payload)>65536:raise ValueError('request too large')
+        with socket.socket(socket.AF_UNIX,socket.SOCK_STREAM) as s:
+            s.settimeout(1810);s.connect(self.socket_path)
+            s.sendall(struct.pack('!I',len(payload))+payload)
+            def exact(n):
+                chunks=[]
+                while n:
+                    chunk=s.recv(min(n,1024*1024))
+                    if not chunk:raise RuntimeError('RPC interrupted; do not replay action with a new ID')
+                    chunks.append(chunk);n-=len(chunk)
+                return b''.join(chunks)
+            length=struct.unpack('!I',exact(4))[0]
+            if length>64*1024*1024:raise RuntimeError('response too large')
+            result=json.loads(exact(length),object_hook=array_hook)
+        if not result['ok']:raise RuntimeError(result['error'])
+        return result['result']
+
+    def next_episode(self,request_id=None):return self.call('next_episode',request_id=request_id)
+    def latest_observation(self):return self.call('latest_observation')
+    def execute_actions(self,actions,request_id=None):
+        return self.call('execute_actions',{'actions':np.asarray(actions).tolist()},request_id)
+    def finish_episode(self):return self.call('finish_episode')
+    def budget_status(self):return self.call('budget_status')
+    def rehearse(self,candidate='candidate',request_id=None):
+        return self.call('rehearse',{'candidate':candidate},request_id)
+    def score_rehearsal(self,episode,visible_success,evidence):
+        return self.call('score_rehearsal',{'episode':episode,'visible_success':visible_success,'evidence':evidence})
+    def freeze(self):return self.call('freeze')
+
+    def read_demo(self,demo_id,start=0,stop=None,fields=None):
+        import h5py
+        manifest=json.loads(self.manifest.read_text())
+        if manifest.get('status') not in ('released','released_for_offline_analysis'):
+            raise RuntimeError('demonstrations not released by trusted preflight')
+        entries={e['demo_id']:e for e in manifest['episodes']}
+        if demo_id not in entries:raise ValueError('unauthorized demo ID')
+        entry=entries[demo_id]
+        allowed={f['path'] for f in entry['fields']}
+        fields=sorted(allowed) if fields is None else fields
+        if not set(fields)<=allowed:raise ValueError('unauthorized demo field')
+        stop=entry['length']+1 if stop is None else stop
+        if not isinstance(start,int) or not isinstance(stop,int) or not 0<=start<stop<=entry['length']+1:
+            raise ValueError('invalid frame interval')
+        path=(self.demo_root/entry['file']).resolve()
+        if path.parent!=self.demo_root.resolve():raise ValueError('invalid demo file')
+        with h5py.File(path,'r') as f:
+            return {field:f[field][start:min(stop,len(f[field]))] for field in fields}

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/controller.py

@@ -0,0 +1,62 @@
+"""Legal D20 geometry-conditioned trajectory interpolation with joint feedback."""
+from pathlib import Path
+import numpy as np
+from geometry import handle_feature
+
+class Controller:
+ def __init__(self):
+  with np.load(Path(__file__).with_name('trajectories.npz')) as d:self.data={k:d[k] for k in d.files}
+  self.features=np.array([handle_feature(self.data[f'top_camera{i}']) for i in range(20)])
+  with np.load(Path(__file__).with_name('feedback.npz')) as d:self.feedback={k:d[k] for k in d.files}
+  self.reset_episode()
+ def reset_episode(self):
+  self.last_step=0;self.phase=0;self.age=0;self.progress=0;self.actions=None
+ def prepare(self,obs):
+  f=handle_feature(obs['rgb']['top_camera']);dist=np.linalg.norm(self.features-f,axis=1);ix=np.argsort(dist)[:8]
+  # Locally affine interpolation of measured handle endpoints; no hidden scene inputs.
+  X=np.c_[np.ones(len(ix)),(self.features[ix]-f)*20]
+  reg=np.diag(0.0005+0.01*(dist[ix]/max(dist[ix][-1],1e-6))**2)
+  w=np.linalg.solve(X@X.T+reg,X@np.array([1.,0,0,0,0]));w/=w.sum()
+  self.weights=w;self.neighbors=ix
+  aa=[];qq=[]
+  for i in ix:
+   a=self.data[f'a{i}'];q=self.data[f'q{i}'];c0=np.flatnonzero(np.diff(a[:,6]>0))+1;c1=np.flatnonzero(np.diff(a[:,13]>0))+1
+   old=[0,c0[0],c1[0],c0[1],len(a)];new=[0,103,263,303,423]
+   ts=np.interp(np.arange(500),new,old);ts=np.minimum(ts,len(a)-1)
+   ai=np.stack([np.interp(ts,np.arange(len(a)),a[:,k]) for k in range(14)],1)
+   qi=np.stack([np.interp(ts,np.arange(len(q)),q[:,k]) for k in range(18)],1)
+   for l in range(4):ai[new[l]:new[l+1],:6]*=(old[l+1]-old[l])/(new[l+1]-new[l]);ai[new[l]:new[l+1],7:13]*=(old[l+1]-old[l])/(new[l+1]-new[l])
+   aa.append(ai);qq.append(qi)
+  self.actions=np.einsum('i,itj->tj',w,np.array(aa));self.targets=np.einsum('i,itj->tj',w,np.array(qq))
+  self.actions[423:,:]=0
+  self.actions[:,6]=np.where((np.arange(500)>=103)&(np.arange(500)<303),-1,1)
+  self.actions[:,13]=np.where(np.arange(500)>=263,-1,1)
+ def act(self,obs):
+  # Both the interactive and standalone observation envelopes are accepted.
+  if 'observation' in obs:obs=dict(obs['observation'],step_id=obs['step_id'])
+  step=int(obs['step_id'])
+  if step>=500:return None
+  if self.actions is None:self.prepare(obs)
+  t=step-1;self.last_step=step;self.phase=int(np.searchsorted([103,263,303],t,side='right'));self.age=t;self.progress=t/423
+  a=self.actions[t].copy()
+  q=np.concatenate([np.asarray(obs['proprioception'][f'agent/panda-{r}/qpos']).reshape(-1) for r in range(2)])
+  for r in range(2):
+   current=q[r*9:r*9+7];target=self.targets[t,r*9:r*9+7]
+   bank=self.feedback;distance=np.mean((bank[f'q{r}']-current)**2,axis=1);ix=np.argsort(distance)[:400]
+   A=bank[f'a{r}'][ix];DQ=bank[f'dq{r}'][ix]
+   B=np.linalg.solve(A.T@A+np.eye(6)*0.001,A.T@DQ)
+   correction=np.linalg.solve(B@B.T+np.eye(6)*0.0003,B@(target-current))*0.18
+   correction=np.clip(correction,-0.08,0.08)
+   # Never introduce an unobserved rotation axis.
+   correction[np.max(np.abs(A),axis=0)<1e-5]=0
+   a[r*7:r*7+6]+=correction
+  return np.clip(a,-1,1).astype(np.float32)
+_controller=None
+def reset_episode():
+ global _controller
+ if _controller is None:_controller=Controller()
+ else:_controller.reset_episode()
+def act(obs):
+ global _controller
+ if _controller is None:reset_episode()
+ return _controller.act(obs)

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/demo_manifest.json

@@ -0,0 +1,2168 @@
+{
+  "task_id": "DualArmHammerHandover-v1",
+  "status": "released",
+  "selection_rule": "prefer existing pinned D20, otherwise numeric trajectory ID sort before reading data",
+  "lineage_status": "pinned_comparator",
+  "episodes": [
+    {
+      "demo_id": "demo_00",
+      "file": "demo_00.h5",
+      "length": 430,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            430,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            430,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            431,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            431,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            431,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            431,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            431,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            431,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            431,
+            240,
+            320,
+            3
+          ],
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+        {
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+          "dtype": "uint8"
+        }
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+    }
+  ]
+}

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/environment.lock

@@ -0,0 +1,249 @@
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agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/findings.md

@@ -0,0 +1,26 @@
+# D20-E20 findings
+
+Only this task's 20 authorized demonstrations and legal API exploration were used. No neural weights, other task data, hidden simulator state, seeds, direct reset, benchmark source, or network model are used. Original D20 analysis is preserved in /workspace/demo_analysis.md; task audit artifacts are in /workspace/exploration.
+
+## Localization and pose
+Initial top-camera white-handle segmentation, connected components, and principal-axis endpoints distinguish image-space location and yaw. Eight nearby demonstrations supply locally affine, phase-aligned trajectories. This resolved the observed baseline pickup miss. No calibrated metric object pose or target height is claimed. Joint targets and transfer height are demonstration-derived. Occlusion and extrapolation remain limitations.
+
+## Closed-loop control
+Current Panda qpos selects 400 nearby legal demo motion samples per arm. Ridge regression fits action-to-joint motion; a damped inverse provides bounded corrections toward interpolated joint targets. Axes absent from nearby demonstrations get zero correction. This is deterministic numerical regression, not neural training or a task simulator. The program uses live proprioception each action; visual localization currently occurs only at initialization.
+
+## Grasp and timing evidence
+Episode 1's original coarse exemplar policy closed beside the handle and lifted empty. Episode 2 returned native success at step 32 before visible handover, so no visual success is claimed. Episode 3 (geometric interpolation) and episode 4 (plus feedback) showed retained donor pickups at diagnostic step 181 using four-step action holds. These were not independent full-task rehearsals.
+
+Independent rehearsals 5–8 of the original closed-loop schedule all returned valid native success at steps 402, 393, 396, 391. Final front images show receiver retention with donor withdrawn. Final receiver fingers were approximately 0.0032–0.0037 m each, consistent with holding the handle. Exact prior bundle hash: 0652f474e20835f7a5b5baecfeb01c3dc71c8218b7b0642189337bd41a595609.
+
+Episodes 9–12 tested roughly doubled motion speed and a shortened 20-native-step shared grasp. All returned native success, but episode 9 ended at step 25 before visible handover. Episode 10 (202 steps) showed retention with substantial handle tilt. Episodes 11 (205 steps) and 12 (200 steps) showed the hammer separated from the receiver. Episode 11 receiver fingers were approximately -0.00014 m, indicating empty closure. These visible failures disqualify the compressed schedule as a reliability improvement despite its native signals.
+
+The current candidate restores the previously successful behavior: donor closes at reference index 103, receiver closes at 263, donor releases at 303, with a 40-native-step shared grasp. Motion ends around reference index 423, and the receiver stays closed through the remaining native horizon. Current runtime code is restored byte-for-byte from the prior successful version; updated documentation and removal of unused legacy assets make a new bundle. Slots 13–20 are reserved for further independent validation, including all four final slots. No result from those future slots is asserted here.
+
+## Recovery and program failures
+Automatic regrasp after a drop is not established. Joint feedback corrects tracking errors but does not certify contact or recover a dropped tool. Conservative shared-grasp dwell is retained after the compressed timing caused visible drops. Native termination can occur before visual completion; native outcomes and visible evidence are recorded separately, without inventing official success.
+
+The exploration driver initially attempted a chunk after native termination; the API rejected it, and termination checks were added. An indentation error in the driver was repaired before issuing episode-4 actions, resuming the same active slot. Episode-1 buffered action records were lost on manual stop; sampled RGB/proprioception and exact baseline source remain. Later interactive commands were logged before execution. Episode-9 descriptive manifest had a stale controller SHA; this did not prevent the independent program from running and was corrected in later bundles. All independent programs through episode 12 completed validly.
+
+## Offline package and evidence
+Runtime needs controller.py, geometry.py, trajectories.npz, feedback.npz, NumPy and OpenCV only. Every compiled example derives from this task's D20 manifest. The runner supplies verified demo files/manifest and environment lock. Original and fast versions are archived under exploration/bundle_v3 and exploration/bundle_fast. Rehearsal JSONs record exact bundle hashes and native outcomes; final legal observations and front/top images are retained. No evaluation outputs are accessed. If native success is absent, ambiguous visible evidence must be scored false through API.score_rehearsal.

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/freeze_manifest.json

@@ -0,0 +1,179 @@
+{
+  "files": {
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+    "demo_manifest.json": "19cf113417351204fb2bb7ecc0c6bc6ce0aa2793868778e44fb221d712bf126b",
+    "demonstrations_readonly/demo_00.h5": "6e3673b0eb73aa718be4c7dc7f4e2bf5739de118288dfbdc5c2c1c20598149d4",
+    "demonstrations_readonly/demo_01.h5": "83f171b61b50ce94209a2191e9aff1e54e52e6a67d1057dcfffe5a7cb94e8452",
+    "demonstrations_readonly/demo_02.h5": "1af302eb07461e06400892f3ee742468a7ce4e80b898468a0d13d868385dadb0",
+    "demonstrations_readonly/demo_03.h5": "631f570eb96aaadad2bd1455efea1396a987cca12acb873433b506abcce53170",
+    "demonstrations_readonly/demo_04.h5": "45a401536485f2228fc2401017fe08358f446f2e19903b7bc64b3fc7d9e5c279",
+    "demonstrations_readonly/demo_05.h5": "a7dc98e3e0b567ac3064aac9484bd2c5ac9b39df1d984f393417a96985f0e296",
+    "demonstrations_readonly/demo_06.h5": "0ea85e37983fb2fefa09f274f229d1501e919493f8977a02fed34ea24c920e04",
+    "demonstrations_readonly/demo_07.h5": "d04ebe62652b8e09d01002fdbabbde0ce02c6a73f77888b3e9418ca0bd5ba7b1",
+    "demonstrations_readonly/demo_08.h5": "0c5936114fc5fdfd9427de98ea167a9e21e8e80e239813b00e1f907f80645e79",
+    "demonstrations_readonly/demo_09.h5": "d4ed5523bda6cfc8703dcece561e5224203392ea7ec058e6deab72c1f1e7ccbc",
+    "demonstrations_readonly/demo_10.h5": "95d26c77f70c96ed8b30b9927aa6101ea8bf9683df020e59fa01db79a3e246cc",
+    "demonstrations_readonly/demo_11.h5": "b6a2128907b0b1eb9b1933a9d569b77c6235faeb6e8a471fca8be08c1c5048f4",
+    "demonstrations_readonly/demo_12.h5": "26a21c94d4969e15d71eec87dc17577c10da276792596f6a27df7735effb5690",
+    "demonstrations_readonly/demo_13.h5": "98def930dc4dc57b4bb211b7348a92ad37c34aa916d390bc5ebdf16dcf952585",
+    "demonstrations_readonly/demo_14.h5": "06a21102741316df5373fefea6f59c6b895abffbbf048c417af9e1514686cb76",
+    "demonstrations_readonly/demo_15.h5": "3a06ca31eaa176e9c62c2f700975a8df15006fb4d3a67c47140f04df9e6442e8",
+    "demonstrations_readonly/demo_16.h5": "ec952c29f39f5f161dbf95be34e1977b8e2c37bf16575c2b4ee0e794dd039bc3",
+    "demonstrations_readonly/demo_17.h5": "ba03b02a8d772e198604ddc77ca8e269e1d8db2fb6a1b77e295a244e37d06d47",
+    "demonstrations_readonly/demo_18.h5": "be54f2b45c0a8f3dfe9b3d13e481f877e49199426319ef9d5ee1b0b12c3d9736",
+    "demonstrations_readonly/demo_19.h5": "11a3676de553c568345622c1ba8c3e62b8fd568aee5cfeabe4b087ca30940bca",
+    "environment.lock": "f9f11f093744865020243315494d48c69e38ff9e67783e7cf5f0a1e5f22b34aa",
+    "feedback.npz": "26939d2a43e8694d4963ea052f0174c722ddd93776864c02a276adabf9d97371",
+    "findings.md": "5c616ddcd4e5b14838adcad3b237a565eba1c62f63fcd167259a2d7ad493b733",
+    "geometry.py": "4cdb343d470416af9b140717834b145f9ed5aa674f8d51349d97e2acc1a23e7c",
+    "handoff.md": "5e4045b10aaa030c13c35b3b9cddb906e47acb35c781074fd48ff48b375ed202",
+    "model_tool_manifest.yaml": "1f5bdc58fb99f452806bbe7a72602f9bc9f29b836c99f60aeabb98a85e85564b",
+    "policy_runner.py": "cd8a30fc763583fb9bf6570414f01d56aba16241c733fc0cdfdfab0f8f27270e",
+    "skills.md": "988677138097555e3a9f797d0ef218a9e1d96912d707ccfde8f988cfa5e0f2bd",
+    "trajectories.npz": "e718a515903a0910ffb1482119d1777f6e011456359c37e574218a1b250d3ef7",
+    "worker_api.py": "88cca5fa5c1c9570ca2bd90e8848f67c09224fa498d94e63634ad191e913c41e"
+  },
+  "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+  "exploration_ledger_hash": "5f6b64a1ab0c64323a9ebcf9616cec59563597b1891157769872796790731839",
+  "entrypoint": "controller.reset_episode(); controller.act(obs)",
+  "selection": {
+    "rule": "exact-version success rate, valid rehearsal count, recency",
+    "rehearsals": [
+      {
+        "bundle_hash": "0652f474e20835f7a5b5baecfeb01c3dc71c8218b7b0642189337bd41a595609",
+        "episode": 5,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "0652f474e20835f7a5b5baecfeb01c3dc71c8218b7b0642189337bd41a595609",
+        "episode": 6,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "0652f474e20835f7a5b5baecfeb01c3dc71c8218b7b0642189337bd41a595609",
+        "episode": 7,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "0652f474e20835f7a5b5baecfeb01c3dc71c8218b7b0642189337bd41a595609",
+        "episode": 8,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "d0cffe910133fda1f4f7de7f5de9b5ff15c45bf40c3204f046f84bba40bbda5a",
+        "episode": 9,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "c833c001f9f9314b3c9bb91ecdd760566e7e45f5c029d97a0dbf3698cddb78dc",
+        "episode": 10,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "c833c001f9f9314b3c9bb91ecdd760566e7e45f5c029d97a0dbf3698cddb78dc",
+        "episode": 11,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "c833c001f9f9314b3c9bb91ecdd760566e7e45f5c029d97a0dbf3698cddb78dc",
+        "episode": 12,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "episode": 13,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "episode": 14,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "episode": 16,
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 17,
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 18,
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 19,
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 20,
+        "bundle_hash": "5eb2c69c51aaada8a60951ea5b9d48744f4f1c5bfae579b80ab2d9e95f9e0f95",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      }
+    ]
+  }
+}

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/geometry.py

@@ -0,0 +1,13 @@
+import cv2,numpy as np
+
+def handle_feature(im):
+ im=cv2.resize(np.asarray(im).squeeze(),(240,320),interpolation=cv2.INTER_AREA)
+ mask=(im.min(2)>170)&((im.max(2).astype(float)-im.min(2))<45)
+ mask[:112]=False;mask[194:]=False;mask[:,:60]=False;mask[:,180:]=False
+ n,l,stats,cent=cv2.connectedComponentsWithStats(mask.astype('uint8'))
+ if n<2:raise ValueError('No initial handle visible')
+ j=1+np.argmax(stats[1:,4]);y,x=np.where(l==j);pts=np.stack([x,y],axis=1).astype(float)
+ mean=pts.mean(0);_,_,v=np.linalg.svd(pts-mean);axis=v[0]
+ if axis[1]<0:axis=-axis
+ proj=(pts-mean)@axis;lo=mean+axis*np.percentile(proj,2);hi=mean+axis*np.percentile(proj,98)
+ return np.r_[lo,hi]/np.array([240,320,240,320])

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/handoff.md

@@ -0,0 +1,9 @@
+# Offline handoff
+
+controller.py exposes reset_episode() and act(obs), plus Controller. Supply the legal observation dictionary with top_camera RGB, Panda qpos paths and step_id, or the worker API observation envelope. The runner owns the initial hold; act generates one bounded float32 14-vector in panda-0/panda-1 order and returns None at native step 500. Initial visual conditioning is followed by live joint feedback each step.
+
+Required runtime files: controller.py, geometry.py, trajectories.npz, feedback.npz. Dependencies: Python, NumPy, OpenCV. All compiled examples come from this task's 20 authorized demonstrations. No external service or additional model is needed. The runner adds verified demo inputs and environment lock.
+
+Current code restores the original schedule that produced independently successful retained handovers in episodes 5–8. It preserves 40 native steps between receiver closure and donor release. The faster variant was rejected after visible drops despite native success signals. Findings document localization, pose, grasp, timing, recovery limits, and tooling failures.
+
+The remaining slots through 20 will validate this exact bundle independently, including mandatory episodes 19 and 20. Final outcomes and freeze receipt are saved outside the immutable bundle under /workspace/exploration. Do not modify behavior after the final rehearsal. Only the API.freeze-selected independently rehearsed exact bundle is submitted.

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/model_tool_manifest.yaml

@@ -0,0 +1,110 @@
+{
+  "stage": "D20_E20_final_conservative_candidate",
+  "status": "restored_independently_successful_behavior_pending_final_repeats",
+  "exploration_episodes_used": 12,
+  "demonstrations_used": [
+    "demo_00",
+    "demo_01",
+    "demo_02",
+    "demo_03",
+    "demo_04",
+    "demo_05",
+    "demo_06",
+    "demo_07",
+    "demo_08",
+    "demo_09",
+    "demo_10",
+    "demo_11",
+    "demo_12",
+    "demo_13",
+    "demo_14",
+    "demo_15",
+    "demo_16",
+    "demo_17",
+    "demo_18",
+    "demo_19"
+  ],
+  "provenance": {
+    "source": "authorized /demonstrations sanitized RGB, proprioception, expert actions",
+    "access": "/worker_api.py API.read_demo only",
+    "contract_sha256": "9896d6e8e8a42a3b0cfe167f961f385a79eabc90c2d635f97e860fd39d4b87c0",
+    "input_manifest_sha256": "d841bec2cc06f203b0ea5bfb0264f8aaa11d35f6466824c5b68e8ba737b23768",
+    "verified_manifest": "runner supplied separately",
+    "common_environment_lock": "runner supplied separately"
+  },
+  "tools_used": [
+    "functions.exec",
+    "exec_command (bash, Python, cat, mkdir, find)",
+    "view_image",
+    "NumPy",
+    "OpenCV image resize/contact sheets",
+    "h5py via API.read_demo",
+    "Python standard library",
+    "write_stdin (poll offline scripts)",
+    "Python py_compile (syntax checks)"
+  ],
+  "versions": {
+    "python": "3.10.20 (main, Mar 11 2026, 17:46:40) [GCC 14.3.0]",
+    "numpy": "2.2.6",
+    "opencv-python": "4.13.0.92",
+    "h5py": "3.16.0"
+  },
+  "runtime_dependencies": [
+    "numpy",
+    "opencv-python"
+  ],
+  "offline_dependencies": [
+    "h5py"
+  ],
+  "model": {
+    "coding_model": "provider identity/version not exposed in workspace; existing model unchanged",
+    "neural_finetuning": false,
+    "pretrained_tool_weights": [],
+    "frozen_weight_references": [],
+    "note": "No additional model weights loaded or altered; exemplar bank is deterministic legal-data compilation, not neural weights"
+  },
+  "known_limitations": [
+    "Initial image-space localization only; no calibrated metric pose",
+    "No certified contact detector or established dropped-tool recovery",
+    "Native success occasionally disagrees with final visible task evidence",
+    "Local interpolation can extrapolate outside demo coverage"
+  ],
+  "artifacts": {
+    "trajectories.npz": "All 20 authorized action/proprioception sequences and initial top/front RGB",
+    "feedback.npz": "Authorized action/consecutive-qpos motion pairs",
+    "geometry.py": "Deterministic image-space handle segmentation"
+  },
+  "artifact_sha256": {
+    "trajectories.npz": "e718a515903a0910ffb1482119d1777f6e011456359c37e574218a1b250d3ef7",
+    "geometry.py": "4cdb343d470416af9b140717834b145f9ed5aa674f8d51349d97e2acc1a23e7c",
+    "controller.py": "20439c443894bf713a48733af8651789f2b2717ca61247ebd1a43d8c023ac9c3",
+    "feedback.npz": "26939d2a43e8694d4963ea052f0174c722ddd93776864c02a276adabf9d97371"
+  },
+  "validation": {
+    "prior_same_behavior_independent_native_and_visible_success": [
+      5,
+      6,
+      7,
+      8
+    ],
+    "rejected_fast_timing_visible_drops": [
+      11,
+      12
+    ],
+    "early_native_signals_without_visible_handover": [
+      2,
+      9
+    ],
+    "planned_remaining_independent_rehearsals": [
+      13,
+      14,
+      15,
+      16,
+      17,
+      18,
+      19,
+      20
+    ],
+    "all_independent_programs_through_12_valid": true
+  }
+}

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/policy_runner.py

@@ -0,0 +1,25 @@
+"""Frozen-program entry point; no development agent and no reset capability."""
+import importlib.util
+import sys
+
+sys.path.insert(0,'/artifact')
+sys.path.insert(0,'/')
+from worker_api import API
+
+
+def main():
+    spec=importlib.util.spec_from_file_location('controller','/artifact/controller.py')
+    module=importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    module.reset_episode()
+    api=API()
+    result=api.latest_observation()
+    while not (result['terminated'] or result['truncated'] or result['horizon_reached']):
+        obs=dict(result['observation'],step_id=result['step_id'])
+        action=module.act(obs)
+        if action is None:break
+        response=api.execute_actions([action])
+        result=response['steps'][-1]
+
+
+if __name__=='__main__':main()

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/skills.md

@@ -0,0 +1,5 @@
+# Validated capabilities and limits
+
+Deterministic initial handle localization from legal top RGB; image-space location/yaw conditioning through eight locally interpolated demonstrations; live qpos feedback fitted from this task's authorized motion pairs; donor pickup, lift/reorientation, receiver approach, 40-native-step shared grasp, donor release and withdrawal, and retained receiver closure. The same runtime behavior achieved four independent native-successful and visibly retained handovers in rehearsals 5–8.
+
+The final program restores that conservative schedule after a faster variant caused visible drops. No calibrated metric pose, contact certainty, hidden target height, general recovery, or visual success classifier is claimed. A failed regrasp is not automatically recovered. No neural weights are changed, and runtime requires no network, model agent, task simulator, or other task artifacts.

agent_d20_e20/frozen_20261102/tasks/DualArmHammerHandover-v1/frozen/worker_api.py

@@ -0,0 +1,81 @@
+"""Standalone worker client; safe to mount without the runner package."""
+import base64
+import json
+import socket
+import struct
+import uuid
+import os
+from pathlib import Path
+
+import numpy as np
+
+
+def array_hook(value):
+    if value.get('__array__') is True:
+        dtype=np.dtype(value['dtype'])
+        if dtype.kind not in 'bifu':raise ValueError('non-numeric array from service')
+        raw=base64.b64decode(value['data'],validate=True)
+        return np.frombuffer(raw,dtype=dtype).reshape(value['shape']).copy()
+    return value
+
+
+class API:
+    def __init__(self,socket_path='/rpc/control.sock',demo_root='/demonstrations',manifest='/demo_manifest.json'):
+        self.socket_path=socket_path
+        self.demo_root=Path(demo_root)
+        self.manifest=Path(manifest)
+
+    def call(self,method,arguments=None,request_id=None):
+        # Caller can persist a request ID before sending. Never automatically retry.
+        request_id=request_id or str(uuid.uuid4())
+        request={'request_id':request_id,'method':method,'arguments':arguments or {}}
+        if os.environ.get('AGENT_E20_RPC_TOKEN'):
+            request['token']=os.environ['AGENT_E20_RPC_TOKEN']
+        payload=json.dumps(request,allow_nan=False).encode()
+        if len(payload)>65536:raise ValueError('request too large')
+        with socket.socket(socket.AF_UNIX,socket.SOCK_STREAM) as s:
+            s.settimeout(1810);s.connect(self.socket_path)
+            s.sendall(struct.pack('!I',len(payload))+payload)
+            def exact(n):
+                chunks=[]
+                while n:
+                    chunk=s.recv(min(n,1024*1024))
+                    if not chunk:raise RuntimeError('RPC interrupted; do not replay action with a new ID')
+                    chunks.append(chunk);n-=len(chunk)
+                return b''.join(chunks)
+            length=struct.unpack('!I',exact(4))[0]
+            if length>64*1024*1024:raise RuntimeError('response too large')
+            result=json.loads(exact(length),object_hook=array_hook)
+        if not result['ok']:raise RuntimeError(result['error'])
+        return result['result']
+
+    def next_episode(self,request_id=None):return self.call('next_episode',request_id=request_id)
+    def latest_observation(self):return self.call('latest_observation')
+    def execute_actions(self,actions,request_id=None):
+        return self.call('execute_actions',{'actions':np.asarray(actions).tolist()},request_id)
+    def finish_episode(self):return self.call('finish_episode')
+    def budget_status(self):return self.call('budget_status')
+    def rehearse(self,candidate='candidate',request_id=None):
+        return self.call('rehearse',{'candidate':candidate},request_id)
+    def score_rehearsal(self,episode,visible_success,evidence):
+        return self.call('score_rehearsal',{'episode':episode,'visible_success':visible_success,'evidence':evidence})
+    def freeze(self):return self.call('freeze')
+
+    def read_demo(self,demo_id,start=0,stop=None,fields=None):
+        import h5py
+        manifest=json.loads(self.manifest.read_text())
+        if manifest.get('status') not in ('released','released_for_offline_analysis'):
+            raise RuntimeError('demonstrations not released by trusted preflight')
+        entries={e['demo_id']:e for e in manifest['episodes']}
+        if demo_id not in entries:raise ValueError('unauthorized demo ID')
+        entry=entries[demo_id]
+        allowed={f['path'] for f in entry['fields']}
+        fields=sorted(allowed) if fields is None else fields
+        if not set(fields)<=allowed:raise ValueError('unauthorized demo field')
+        stop=entry['length']+1 if stop is None else stop
+        if not isinstance(start,int) or not isinstance(stop,int) or not 0<=start<stop<=entry['length']+1:
+            raise ValueError('invalid frame interval')
+        path=(self.demo_root/entry['file']).resolve()
+        if path.parent!=self.demo_root.resolve():raise ValueError('invalid demo file')
+        with h5py.File(path,'r') as f:
+            return {field:f[field][start:min(stop,len(f[field]))] for field in fields}

agent_d20_e20/frozen_20261102/tasks/DualArmJugglingBall-v1/frozen/demonstrations_readonly/demo_03.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d56aa11e10fe1dede2e9b29118ff375e9622cb427fc7d41e314b8357979a03ad
+size 170734162

agent_d20_e20/frozen_20261102/tasks/DualArmJugglingBall-v1/frozen/demonstrations_readonly/demo_04.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f3b321cdc749997bf4906e4022684bdd0fe2a4e3773c4bbea740313a410b652
+size 174973488

agent_d20_e20/frozen_20261102/tasks/DualArmJugglingBall-v1/frozen/demonstrations_readonly/demo_08.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61e858727b4ea4e2eeff1c4e156c575f39beaeeede80100f5906c98700623d6b
+size 172804863

agent_d20_e20/frozen_20261102/tasks/DualArmJugglingBall-v1/frozen/demonstrations_readonly/demo_09.h5

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