Add files using upload-large-folder tool

agent_d20_e20/frozen_20261102/tasks/ClutterGrasp-v1/frozen/build_reference.py

@@ -0,0 +1,26 @@
+"""Offline reference compilation from the 20 authorized demos only. No fitting."""
+import importlib.util,json,sys
+from pathlib import Path
+import numpy as np
+sys.path.insert(0,str(Path(__file__).resolve().parent))
+from controller import image_features,CAMERAS
+s=importlib.util.spec_from_file_location('worker_api','/worker_api.py');m=importlib.util.module_from_spec(s);s.loader.exec_module(m);api=m.API()
+manifest=json.load(open('/demo_manifest.json'))
+assert len(manifest['episodes'])==20
+rows={k:[] for k in ['features','qpos','qvel','actions','demo','frame']};audit=[]
+for di,e in enumerate(manifest['episodes']):
+ n=e['length'];fields=['actions','obs/agent/qpos','obs/agent/qvel'];d=api.read_demo(e['demo_id'],fields=fields)
+ indices=list(range(21,n,2));audit.append({'demo_id':e['demo_id'],'computational_rgb_frames':indices,'label_intervals':[[t,min(t+2,n)] for t in indices]})
+ for start in range(21,n,64):
+  stop=min(n,start+64)
+  imgs=api.read_demo(e['demo_id'],start,stop,fields=['obs/sensor_data/'+c+'/rgb' for c in CAMERAS])
+  for t in range(start,stop,2):
+   rows['features'].append(image_features({c:imgs['obs/sensor_data/'+c+'/rgb'][t-start] for c in CAMERAS}))
+   rows['qpos'].append(d['obs/agent/qpos'][t]);rows['qvel'].append(d['obs/agent/qvel'][t])
+   rows['actions'].append(d['actions'][t:min(t+2,n)].mean(0));rows['demo'].append(di);rows['frame'].append(t)
+ print(e['demo_id'],len(indices),flush=True)
+b={k:np.asarray(v,dtype=np.int32 if k in ['demo','frame'] else np.float32) for k,v in rows.items()}
+b['qscale']=np.maximum(np.std(b['qpos'],axis=0),.20);b['vscale']=np.maximum(np.std(b['qvel'],axis=0),.10)
+np.savez_compressed(Path(__file__).with_name('reference_bank.npz'),**b)
+json.dump(audit,open(Path(__file__).with_name('reference_index.json'),'w'),indent=2)
+print('bank',len(b['actions']),flush=True)

agent_d20_e20/frozen_20261102/tasks/ClutterGrasp-v1/frozen/check_synthetic.py

@@ -0,0 +1,43 @@
+"""Syntax/data-integrity and synthetic-input checks only; no environment."""
+import json,sys,time,py_compile
+from pathlib import Path
+import numpy as np
+sys.path.insert(0,str(Path(__file__).resolve().parent))
+import controller as c
+root=Path(__file__).resolve().parent
+for name in ['controller.py','build_reference.py','check_synthetic.py']:
+ py_compile.compile(str(root/name),doraise=True)
+checks=[]
+def ok(name,condition):
+ assert condition,name
+ checks.append(name)
+def obs(color=100,batch=False):
+ rgb={k:np.full((320,240,3) if k=='top_camera' else (240,320,3),color,np.uint8) for k in c.CAMERAS}
+ if batch:rgb={k:v[None] for k,v in rgb.items()}
+ return {'rgb':rgb,'proprioception':{'agent/qpos':np.zeros(23,np.float32),'agent/qvel':np.zeros(23,np.float32)},'instruction':'The robot should grasp the target chips can from the clutter.','step_id':1}
+x=c.Controller();b=x.bank
+ok('bank covers all 20 demo IDs',np.array_equal(np.unique(b['demo']),np.arange(20)))
+ok('reference vectors legal',b['actions'].shape==(4696,22) and np.isfinite(b['actions']).all() and np.max(abs(b['actions']))<=1)
+ok('reference features finite',np.isfinite(b['features']).all())
+t=time.perf_counter();a=x.act(obs());latency=time.perf_counter()-t
+ok('synthetic runtime shape and bounds',a.shape==(22,) and a.dtype==np.float32 and np.isfinite(a).all() and max(abs(a))<=1)
+x.reset_episode();a2=x.act(obs(batch=True));ok('HWC and 1HWC equivalence',np.array_equal(a,a2))
+y=obs();y['proprioception']['agent/qpos'][0]=np.nan;ok('invalid numeric input returns zeros',np.array_equal(x.act(y),np.zeros(22)))
+y=obs();del y['rgb']['left_camera'];ok('missing camera returns zeros',np.array_equal(x.act(y),np.zeros(22)))
+y=obs();y['step_id']=2000;ok('native horizon terminates',x.act(y) is None)
+x.visits[:]=7;x.last_index=5;x.reset_episode();ok('reset clears temporal state',x.last_index is None and not x.visits.any())
+# A wholly fabricated reference fixture checks observation sensitivity, not task skill.
+def fixture(rgb_difference):
+ x=c.Controller.__new__(c.Controller)
+ o0,o1=obs(50),obs(200 if rgb_difference else 50)
+ q=np.zeros((2,23),np.float32)
+ if not rgb_difference:q[1,:7]=.5;o1['proprioception']['agent/qpos'][:7]=.5
+ act=np.zeros((2,22),np.float32);act[:,0]=[.08,-.08]
+ x.bank={'features':np.stack([c.image_features(o0['rgb']),c.image_features(o1['rgb'])]),'qpos':q,'qvel':np.zeros_like(q),'qscale':np.full(23,.2,np.float32),'vscale':np.ones(23,np.float32),'actions':act,'demo':np.array([0,1]),'frame':np.array([21,21])}
+ x.reset_episode();a=x.act(o0);x.reset_episode();b=x.act(o1)
+ return a,b
+for modality in ['RGB','proprioception']:
+ a,b=fixture(modality=='RGB');ok('current '+modality+' changes selected action in synthetic fixture',a[0]>.05 and b[0]<-.05)
+c.reset_episode();ok('public module entry point',c.act(obs()).shape==(22,))
+result={'status':'passed','scope':'syntax, reference integrity, synthetic inputs only','checks':checks,'synthetic_actual_bank_call_seconds':latency,'environment_calls':0,'rehearsals':0,'task_success':'unknown'}
+(root/'synthetic_check_results.json').write_text(json.dumps(result,indent=2)+'\n');print(json.dumps(result,indent=2))

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

@@ -0,0 +1,129 @@
+"""Unrehearsed D20 draft: nonparametric RGB/proprio action retrieval.
+No environment imports, learned neural weights, seed lookup, or timed replay.
+"""
+from pathlib import Path
+import numpy as np
+import cv2
+from localizer import Localizer
+
+CAMERAS = ('front_camera', 'left_camera', 'robot_camera', 'top_camera')
+# Normalized crops selected by visual inspection; extrinsics remain unknown.
+CROPS = ((.18,.32,.65,.90), (.28,.25,.95,.95), (0.,0.,1.,1.), (.10,.40,.95,.98))
+
+def image_features(rgb):
+    out = []
+    for name, (x0,y0,x1,y1) in zip(CAMERAS,CROPS):
+        im = np.asarray(rgb[name])
+        if im.ndim == 4 and im.shape[0] == 1:
+            im = im[0]
+        if im.ndim != 3 or im.shape[2] != 3 or min(im.shape[:2]) < 16 or im.dtype != np.uint8:
+            raise ValueError('Expected uint8 RGB HWC or 1HWC')
+        h,w = im.shape[:2]
+        crop = im[int(y0*h):int(y1*h),int(x0*w):int(x1*w)]
+        small = cv2.resize(crop,(12,12),interpolation=cv2.INTER_AREA).astype(np.float32)/255.
+        # Red excess helps distinguish the chips package; it is not an object detector.
+        red = np.maximum(small[:,:,0] - np.maximum(small[:,:,1],small[:,:,2]),0.)
+        out.append(np.concatenate([small,red[:,:,None]],axis=2).reshape(-1))
+    return np.stack(out)
+
+class Controller:
+    def __init__(self):
+        with np.load(Path(__file__).with_name('reference_bank.npz'),allow_pickle=False) as z:
+            self.bank = {k:z[k] for k in z.files}
+        self.localizer = Localizer()
+        self.reset_episode()
+
+    def reset_episode(self):
+        self.target_hint = None
+        self.scene_penalty = np.zeros(20,dtype=np.float32)
+        self.calls = 0
+        self.last_index = None
+        self.visits = np.zeros(len(self.bank['actions']),dtype=np.int32)
+        self.status = 'reset; untested draft'
+
+    def act(self, obs):
+        # The charged initial hold is owned by the runner. No warmup is inserted.
+        try:
+            self.calls += 1
+            if 'observation' in obs:
+                outer = obs
+                obs = dict(outer['observation'])
+                obs['step_id'] = outer.get('step_id', self.calls)
+            step = int(obs.get('step_id', self.calls))
+            if step >= 650:
+                self.status = 'native horizon reached; no success claim'
+                return None
+            if step <= 21:
+                self.status = 'settling clutter; legal zero deltas'
+                return np.zeros(22,dtype=np.float32)
+            if step < 1:
+                raise ValueError('step_id must start at one after runner hold')
+            p = obs['proprioception']
+            q = np.asarray(p['agent/qpos'],dtype=np.float32).reshape(-1)
+            v = np.asarray(p['agent/qvel'],dtype=np.float32).reshape(-1)
+            if q.shape != (23,) or v.shape != (23,) or not np.isfinite(q).all() or not np.isfinite(v).all():
+                raise ValueError('Invalid proprioception')
+            f = image_features(obs['rgb'])
+        except (KeyError,ValueError,TypeError,OverflowError):
+            self.status = 'invalid legal observation; zero deltas'
+            return np.zeros(22,dtype=np.float32)
+        b = self.bank
+        if self.target_hint is None and step <= 80:
+            found = self.localizer.locate(obs['rgb']['left_camera'])
+            if found is not None and found['matches'] >= 5:
+                self.target_hint = found
+                lb = self.localizer.bank
+                axes = np.array([lb[f'axis_{i}'] for i in range(20)])
+                delta = (lb['centers']-np.array(found['center']))/100.
+                self.scene_penalty = .5*np.sum(delta*delta,axis=1) + .7*(1.-np.abs(axes@np.array(found['axis'])))
+        qerr = ((b['qpos']-q)/b['qscale'])**2
+        arm = np.mean(qerr[:,:7],axis=1)
+        hand = np.mean(qerr[:,7:],axis=1)
+        vel = np.mean(np.minimum(((b['qvel']-v)/b['vscale'])**2,9.),axis=1)
+        vd = np.mean((b['features']-f)**2,axis=2)
+        # Wrist camera has rendering artifacts and receives lower weight.
+        visual = vd @ np.array([.30,.30,.10,.30],dtype=np.float32)
+        red_dist = np.mean((b['features'][:,:,3::4]-f[:,3::4])**2,axis=(1,2))
+        cost = 12.*visual + 30.*red_dist + .25*arm + .18*hand + .015*vel
+        cost += self.scene_penalty[b['demo']]
+        base_cost = cost.copy()
+        if self.last_index is not None:
+            j = self.last_index
+            same = b['demo'] == b['demo'][j]
+            distance = np.abs(b['frame']-b['frame'][j])
+            cost += np.where(same,.015*np.minimum(distance/40.,4.),.12)
+        cost += .001*np.minimum(self.visits,80)
+        cost += np.where(np.isin(b['demo'],[0,3,7,12,19]),100.,0.)
+        j = int(np.argmin(cost))
+        # Deliberately uncalibrated support checks; do not improvise large motions.
+        arm_rms = float(np.sqrt(np.mean((b['qpos'][j,:7]-q[:7])**2)))
+        if visual[j] > .12 or arm_rms > .65 or base_cost[j] > 2.5:
+            self.last_index = None
+            self.status = 'outside reference coverage; zero deltas'
+            return np.zeros(22,dtype=np.float32)
+        # Only blend nearby samples with compatible arm commands and hand pose.
+        eligible = np.flatnonzero((np.linalg.norm(b['actions'][:,:6]-b['actions'][j,:6],axis=1)<.12)
+                                 & (np.mean((b['qpos'][:,7:]-b['qpos'][j,7:])**2,axis=1)<.04))
+        ids = np.array([j])
+        weight = np.exp(-(cost[ids]-np.min(cost[ids]))/.025)
+        action = np.sum(b['actions'][ids]*weight[:,None],axis=0)/weight.sum()
+        self.visits[j] += 1
+        self.last_index = j
+        self.status = 'observation-conditioned retrieval; completion unknown'
+        # Single contract action part: arm translation, arm rotation, 16 dex deltas.
+        return np.clip(action,-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/ClutterGrasp-v1/frozen/demo_manifest.json

@@ -0,0 +1,1488 @@
+{
+  "task_id": "ClutterGrasp-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": 936,
+      "fields": [
+        {
+          "path": "actions",
+          "shape": [
+            936,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qpos",
+          "shape": [
+            937,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qvel",
+          "shape": [
+            937,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            937,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            937,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera/rgb",
+          "shape": [
+            937,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            937,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "dab48831f079e1c40fa3e9696aa39edff47e4e022d142c7767e2e1535135795e",
+      "source_sha256": "55248d8a5853918dea2736deae5ca3d87cd7a217decc5c5414a1c04ff872db9c",
+      "view_sha256": "bb87560299133fded4b00d572f0e1735abb77f64b225619b7d9ad106e91d0ded"
+    },
+    {
+      "demo_id": "demo_01",
+      "file": "demo_01.h5",
+      "length": 318,
+      "fields": [
+        {
+          "path": "actions",
+          "shape": [
+            318,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qpos",
+          "shape": [
+            319,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qvel",
+          "shape": [
+            319,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            319,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            319,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera/rgb",
+          "shape": [
+            319,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            319,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "5c656f6b5c439c3ee4c9d9edadd53873d0d587ec0dc99a0e5d944cfa3cbb8591",
+      "source_sha256": "ba9f86626955ffb48a871e34470389e09ccff50f66ed00abc39c6204498491ea",
+      "view_sha256": "15050afcb690f2fedd109b0f3b3e7cf27a86e5ec1a27bc7e65a6dade6b017aa0"
+    },
+    {
+      "demo_id": "demo_02",
+      "file": "demo_02.h5",
+      "length": 405,
+      "fields": [
+        {
+          "path": "actions",
+          "shape": [
+            405,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qpos",
+          "shape": [
+            406,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qvel",
+          "shape": [
+            406,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            406,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            406,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera/rgb",
+          "shape": [
+            406,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            406,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "f5ee0d01a13f2ec7fd185109ecccc76ff47c188f2a5fa796528e211c2d36db72",
+      "source_sha256": "a8c9ccca94755fd4eca40f492c37eafb0e696307baeb849e36776bc74e9698d0",
+      "view_sha256": "3adf8a2cdda877ab9148f101dc12306f5e6a27385a0266d4c4dcd210f77ef512"
+    },
+    {
+      "demo_id": "demo_03",
+      "file": "demo_03.h5",
+      "length": 1040,
+      "fields": [
+        {
+          "path": "actions",
+          "shape": [
+            1040,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qpos",
+          "shape": [
+            1041,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qvel",
+          "shape": [
+            1041,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            1041,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            1041,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera/rgb",
+          "shape": [
+            1041,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            1041,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "1afb18dfeb547127a15601caef162514e88afadd740e37031973ead8174dccfe",
+      "source_sha256": "982062e5cec6d5ab97fbe1e379fa94ff3ec85ff2e8eb2ca724d0ffda21c287aa",
+      "view_sha256": "11aa54f027b3e9cb5e08b72010dd2df99b64ea687d65b0781c0fec9e2812a4b1"
+    },
+    {
+      "demo_id": "demo_04",
+      "file": "demo_04.h5",
+      "length": 301,
+      "fields": [
+        {
+          "path": "actions",
+          "shape": [
+            301,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qpos",
+          "shape": [
+            302,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/qvel",
+          "shape": [
+            302,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            302,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            302,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera/rgb",
+          "shape": [
+            302,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            302,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "ba85d9b7138933b0bcefe696d61faf6becaac28dd6f8d25ee45e3cbd9c2117b5",
+      "source_sha256": "9357bddce0221366f9b55ea2deb97aff87c197b728bc2e74bb7ca9a7e2375999",
+      "view_sha256": "d4eaad0568dba1bddabf2bbbb31cbbe367c2b46d0828d5cd81866cccebc68a5d"
+    },
+    {
+      "demo_id": "demo_05",
+      "file": "demo_05.h5",
+      "length": 292,
+      "fields": [
+        {
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+}

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

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agent_d20_e20/frozen_20261102/tasks/ClutterGrasp-v1/frozen/findings.md

@@ -0,0 +1,16 @@
+# Offline findings
+
+All 20 authorized demos were analyzed: 9,802 actions, complete proprioception sequences, and five visually inspected timepoints per demo across all four cameras. See ../demo_analysis.md for exact IDs/indices and ../evidence for statistics and contact sheets.
+
+Every sequence begins with 21 zero commands and ends with a 45-command hand-only interval followed by arm motion. The leading zeros are not replayed: the runner provides its own charged hold. Target/clutter arrangements and wrist approaches vary substantially. Long sequences include several reorientations. Wrist images have clipping artifacts.
+
+Most final sampled external views suggest an elevated target, but demo_03 does not clearly establish retained lift. No benchmark success labels or retained-lift duration are available. Action-to-hand-qpos permutation is unknown. Native contract specifies 22 normalized actions at 20 Hz and horizon 2,000; qpos/qvel width is 23.
+
+The draft uses a non-neural observation/action memory. Its coverage thresholds and feature weights are hand-set and unvalidated. It can stall, alias scenes, or blend incompatible commands. No live performance is known and no episodes were spent.
+
+## Live exploration update
+Episodes 1–2 did not show a grasp. Ep1 closed beside the can then lifted empty; ep2 moved toward an edge-positioned can but switched incompatible reference configurations. Driver API envelope bug was fixed during ep1. Current revision adds explicit red-excess cost, restores moderate proprio weighting, and uses a single neighbor command. Wrapper/direct observation support and an internal step fallback are implemented. Current experimental policy stops after 500 steps without claiming success. Evidence and per-episode details are in /workspace/exploration, outside the runtime bundle.
+
+Timing correction for first independent rehearsal: live initial objects visibly settle after step 1, whereas the reference bank starts at demo step 21 after all demonstrations' 21 zero commands. Current variant explicitly holds through step 21, counted within the native budget, then begins observation-conditioned retrieval. This supersedes the earlier no-warmup draft decision. It does not access reset observations or change the runner's charged initial hold.
+
+Episode6 localized short-reference variant reached native success=true and terminated=true at step338. SIFT initial hint had5 inliers and led to demo1-like approach. Interactive action chunks repeated4 steps; this is evidence of task performance for that live test, not independent exact-run qualification. The exact runtime behavior is now submitted for independent rehearsal as episode7. Episode4 remains a valid older independent failure fallback.

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

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+    "demonstrations_readonly/demo_18.h5": "2c9b2ccb54f14a8b2943019ba8f1f063b950dd93aa23551ab17f74853553821f",
+    "demonstrations_readonly/demo_19.h5": "f19ef24f0605a5de54c7f40c53e2e5e7f2b87781190eb17d3e25a6145f49535f",
+    "environment.lock": "f9f11f093744865020243315494d48c69e38ff9e67783e7cf5f0a1e5f22b34aa",
+    "findings.md": "3d227523c7a145f6722005bd05aa7b54a5981f12dcd15074c86a03353c459265",
+    "handoff.md": "4fd6863e950f049fb2af0d228ddbc559fb39a55b345d3b095156c3de808c845b",
+    "localizer.py": "22572067de204558380295e0785d2d5d246e0eb22dfd416d29de08cad9db2a20",
+    "model_tool_manifest.yaml": "d94dc140b81b1cada29f57386e6d18a842a303281f894181c34d7137503ed2f2",
+    "policy_runner.py": "cd8a30fc763583fb9bf6570414f01d56aba16241c733fc0cdfdfab0f8f27270e",
+    "reference_bank.npz": "ee1dce57d4eb359ad2a8be436d0309084d06554b5b66ae846e028dbe819d6e99",
+    "reference_index.json": "7d131d9149bd44611135a0d5907782e40507746feb5d77abd4d17bbe8c81a92b",
+    "skills.md": "1be37edfdfaf3854778c953fb5708da5075d315090c28ccf12e5f09d15c13281",
+    "synthetic_check_results.json": "3c40c782b752d6df436fa8a9b26855a99fb43b42cbfe44526ecc758717b2c833",
+    "target_templates.npz": "3685ea42102b906c4588d2d2427c6e94b0158d95cfdc576efae2aaf5c364d437",
+    "worker_api.py": "88cca5fa5c1c9570ca2bd90e8848f67c09224fa498d94e63634ad191e913c41e"
+  },
+  "bundle_hash": "9acacf6127730bb8b2263bea815d3af534d8b4c18ee43b03d2818cb5adc3da4e",
+  "exploration_ledger_hash": "cb611343649476aa559da3a74b9b1c7c7c7050c4d9824ce051a845f568a1daaf",
+  "entrypoint": "controller.reset_episode(); controller.act(obs)",
+  "selection": {
+    "rule": "exact-version success rate, valid rehearsal count, recency",
+    "rehearsals": [
+      {
+        "bundle_hash": "87748d9f132ec51251b7e2a2e230e1e965871c7ad4701833259b6f3ffcd78dca",
+        "episode": 4,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": false,
+        "valid": true
+      },
+      {
+        "bundle_hash": "9acacf6127730bb8b2263bea815d3af534d8b4c18ee43b03d2818cb5adc3da4e",
+        "episode": 7,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "1ec537f1f76fd717c63618817ae774cb9f1ae8508f300df66928fb86b53d9089",
+        "episode": 13,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": false,
+        "valid": true
+      },
+      {
+        "episode": 14,
+        "bundle_hash": "1ec537f1f76fd717c63618817ae774cb9f1ae8508f300df66928fb86b53d9089",
+        "independent": true,
+        "valid": false,
+        "status": "infrastructure_interrupted",
+        "success": false,
+        "score_source": "interrupted_no_score",
+        "recovery": "durable_rehearsal_request_and_charged_episode; no replay"
+      },
+      {
+        "episode": 15,
+        "bundle_hash": "1ec537f1f76fd717c63618817ae774cb9f1ae8508f300df66928fb86b53d9089",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 16,
+        "bundle_hash": "1ec537f1f76fd717c63618817ae774cb9f1ae8508f300df66928fb86b53d9089",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 17,
+        "bundle_hash": "47059014cae71082fdb0f366ff7081f53df066a4af4f753a99423f7d698ae1d5",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 18,
+        "bundle_hash": "47059014cae71082fdb0f366ff7081f53df066a4af4f753a99423f7d698ae1d5",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 19,
+        "bundle_hash": "47059014cae71082fdb0f366ff7081f53df066a4af4f753a99423f7d698ae1d5",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 20,
+        "bundle_hash": "47059014cae71082fdb0f366ff7081f53df066a4af4f753a99423f7d698ae1d5",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      }
+    ]
+  }
+}

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

@@ -0,0 +1,9 @@
+# Runtime handoff
+
+This task-only candidate is under legal Agent-D20-E20-Compiled exploration. controller.py exports reset_episode and act. Runtime is offline Python, NumPy, OpenCV, reference_bank.npz, localizer.py, and target_templates.npz. No network, model service, neural weights, simulator imports, hidden state, seeds, or other task artifacts are used.
+
+Accept direct legal observations or API-wrapped observations. RGB keys are front_camera, left_camera, robot_camera, top_camera; proprioception keys are agent/qpos and agent/qvel (23 values). Output is a bounded22-dimensional float32 action or None at the policy limit. Native horizon2000 is unchanged. The current experimental policy cap is650; step_id falls back to per-episode act-call count when absent.
+
+Runner hold is step1. Controller sends ordinary charged zero deltas through step21 for clutter settling. Afterwards it retrieves from this task's20 authorized demonstrations. Long/circuitous demonstrations are currently excluded from action selection but preserved in the bank. SIFT target templates from all20 demos' legal left-camera frame21 provide an optional initial scene penalty when sufficiently supported. Image/proprio retrieval remains active at every action. The localizer's scene hint is not a hidden pose and is not a grasp-success detector.
+
+No success claim is made by controller termination. At least one earlier exact version independently ran (episode4), with native success false. Version-specific live failures include missed target alignment, wrong blue-can closure, and discontinuous reference selection. /workspace/exploration and /workspace/versions retain audit evidence, outside the runtime bundle. The final runner must choose only its independently rehearsed exact hashes, not an arbitrary workspace state.

agent_d20_e20/frozen_20261102/tasks/ClutterGrasp-v1/frozen/localizer.py

@@ -0,0 +1,29 @@
+"""Fixed SIFT templates of this task's chips can; no neural model."""
+from pathlib import Path
+import cv2,numpy as np
+class Localizer:
+ def __init__(self):
+  z=np.load(Path(__file__).with_name('target_templates.npz'));self.bank={k:z[k] for k in z.files}
+  self.sift=cv2.SIFT_create(nfeatures=1200,contrastThreshold=.012,edgeThreshold=15);self.matcher=cv2.BFMatcher()
+ def locate(self,rgb):
+  im=np.asarray(rgb);im=im[0] if im.ndim==4 else im
+  im=cv2.resize(cv2.resize(im,(320,240),interpolation=cv2.INTER_AREA),(640,480))
+  k,d=self.sift.detectAndCompute(cv2.cvtColor(im,cv2.COLOR_RGB2GRAY),None)
+  if d is None:return None
+  points=np.array([p.pt for p in k],np.float32);best=None
+  for i in range(20):
+   if len(self.bank[f'des_{i}'])<4:continue
+   matches=self.matcher.knnMatch(self.bank[f'des_{i}'],d,k=2)
+   pairs=[a for a,b in matches if a.distance<.72*b.distance and a.distance<260]
+   pairs=list({p.trainIdx:p for p in sorted(pairs,key=lambda p:-p.distance)}.values())
+   if len(pairs)<4:continue
+   src=self.bank[f'pts_{i}'][[p.queryIdx for p in pairs]];dst=points[[p.trainIdx for p in pairs]]
+   m,mask=cv2.estimateAffinePartial2D(src,dst,method=cv2.RANSAC,ransacReprojThreshold=3,maxIters=1000)
+   if m is None:continue
+   n=int(mask.sum());scale=np.linalg.norm(m[:,0]);center=m[:,2]
+   if n<4 or not .65<scale<1.5 or not (200<center[0]<460 and 160<center[1]<360):continue
+   err=np.mean(np.linalg.norm(src@m[:,:2].T+m[:,2]-dst,axis=1)[mask.ravel()>0]);score=n-.2*err
+   if best is None or score>best['score']:
+    axis=m[:,:2]@self.bank[f'axis_{i}'];axis=axis/np.linalg.norm(axis)
+    best={'center':center.tolist(),'axis':axis.tolist(),'matches':n,'score':float(score),'template':i}
+  return best

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

@@ -0,0 +1,146 @@
+{
+  "stage": "LEGAL E20 EXPLORATION",
+  "status": "experimental; independent rehearsal pending",
+  "task_id": "ClutterGrasp-v1",
+  "model": {
+    "coding_model": "Provider-hosted Codex assistant; exact deployed model/weight identifier not exposed",
+    "weight_updates": false,
+    "additional_neural_models_loaded": [],
+    "frozen_weight_references": [],
+    "note": "No pretrained tool weights loaded or changed; no neural finetuning."
+  },
+  "runtime": {
+    "python": "3.10.20",
+    "numpy": "2.2.6",
+    "cv2_loaded_version": "4.12.0",
+    "required": [
+      "Python standard library",
+      "NumPy",
+      "OpenCV"
+    ],
+    "data_files": [
+      "reference_bank.npz",
+      "target_templates.npz"
+    ],
+    "network_required": false,
+    "code_files": [
+      "controller.py",
+      "localizer.py"
+    ]
+  },
+  "offline_dependencies": {
+    "h5py": "3.16.0",
+    "worker_api": "/worker_api.py API.read_demo only",
+    "installed_distribution_versions": {
+      "numpy": "2.2.6",
+      "h5py": "3.16.0",
+      "opencv-python": "4.13.0.92",
+      "opencv-python-headless": "4.12.0.88",
+      "scipy": "1.15.3",
+      "torch": "2.10.0"
+    },
+    "note": "SciPy and torch versions queried as package metadata only; neither used for controller or analysis. Both OpenCV distributions are installed; loaded cv2 version is authoritative."
+  },
+  "actual_tools": [
+    {
+      "name": "functions.exec / exec_command / write_stdin",
+      "use": "local shell, file authoring, Python offline scripts and synthetic checks",
+      "version": "tool service version not exposed"
+    },
+    {
+      "name": "functions.exec / view_image",
+      "use": "visually inspect all 20 contact sheets",
+      "version": "tool service version not exposed"
+    },
+    {
+      "name": "Python standard library",
+      "use": "importlib, pathlib, json, hashlib, platform, sys, time, py_compile, importlib.metadata"
+    },
+    {
+      "name": "NumPy",
+      "version": "2.2.6",
+      "use": "numeric sequence statistics, handcrafted descriptors, nonparametric retrieval, synthetic inputs"
+    },
+    {
+      "name": "OpenCV",
+      "version": "4.12.0",
+      "use": "image resize/crops, contact sheet rendering, fixed red-excess descriptors"
+    },
+    {
+      "name": "h5py",
+      "version": "3.16.0",
+      "use": "authorized HDF5 reads within API.read_demo"
+    },
+    {
+      "name": "API.read_demo",
+      "sha256": "88cca5fa5c1c9570ca2bd90e8848f67c09224fa498d94e63634ad191e913c41e",
+      "use": "local authorized RGB, qpos/qvel, action reads; no RPC calls"
+    },
+    {
+      "name": "worker_api.API",
+      "use": "budget_status, next_episode, execute_actions, finish_episode, rehearse, score_rehearsal, freeze; only legal task interaction"
+    }
+  ],
+  "provenance": {
+    "contract_sha256": "08a3f60239efe641a9fa77e4d0041e80724741a181371bc11a0a16f1e80741d3",
+    "input_manifest_sha256": "1513b3c32b58886c53b3a9b0460f4b835dba8800ef849ccb7698e910c8d0b077",
+    "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"
+    ],
+    "source": "Only the 20 /demonstrations files named by supplied manifest",
+    "visual_inspection_record": "../demo_analysis.md and ../evidence/statistics.json",
+    "numeric_inspection_record": [
+      "../evidence/statistics.json",
+      "../evidence/action_sequences.json",
+      "../evidence/velocity_statistics.json"
+    ],
+    "reference_computational_indices": "reference_index.json",
+    "reference_samples": 4696,
+    "construction": "build_reference.py; fixed handcrafted image descriptors and stored two-row action means; no optimization or neural fitting",
+    "runner_supplied_later": [
+      "verified demo manifest",
+      "common environment lock"
+    ],
+    "target_templates": "All20 authorized demos, left_camera observation21; manual image-axis masks in exploration/template_annotations.json; fixed SIFT, no neural optimization"
+  },
+  "validation": {
+    "completed_slots": 6,
+    "valid_independent_rehearsal_episodes": [
+      4
+    ],
+    "interactive_native_success_episodes": [
+      6
+    ],
+    "current_behavior_independently_rehearsed": false,
+    "frozen": false
+  },
+  "limits": [
+    "No internet or audited provider proxy request attempted; proxy capability unverified.",
+    "No simulator, hidden states, task-specific expert/controller, or other task data used.",
+    "Initial combined text tool output was truncated; full authorized manifest parsed locally afterward.",
+    "RGB features are coarse; wrist rendering artifacts and target occlusion can cause retrieval ambiguity.",
+    "Hand joint permutation and camera calibration are unknown.",
+    "Coverage thresholds and action blending are uncalibrated; policy may stall or continue moving after a grasp.",
+    "No success detector; None denotes horizon exhaustion only.",
+    "No external weight provenance needed because no additional neural model was loaded."
+  ]
+}

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

@@ -0,0 +1,10 @@
+# Implemented controller mechanisms
+
+- Legally wait for initial clutter settling within the charged native steps.
+- Compare four camera images and23-dimensional joint position/velocity against authorized demonstration observations.
+- Optionally localize the chips can using fixed SIFT descriptors, manually annotated demo target masks, and RANSAC; reject weak matches.
+- Bias reference scenes using supported target center and unsigned image axis; retrieve bounded actions using current image/proprio similarity and continuity.
+- Preserve normalized translation, negative rotation scale, and16 dex joint delta ordering by retaining demonstration action ordering.
+- Stop at the policy limit; hold on unsupported/malformed observations.
+
+These are mechanisms, not verified task capabilities. No calibrated3D pose, contact estimator, verified grasp detector, or reliable recovery has been established. No external skill or additional neural model is used.

agent_d20_e20/frozen_20261102/tasks/ClutterGrasp-v1/frozen/synthetic_check_results.json

@@ -0,0 +1,22 @@
+{
+  "status": "passed",
+  "scope": "syntax, reference integrity, synthetic inputs only",
+  "checks": [
+    "bank covers all 20 demo IDs",
+    "reference vectors legal",
+    "reference features finite",
+    "synthetic runtime shape and bounds",
+    "HWC and 1HWC equivalence",
+    "invalid numeric input returns zeros",
+    "missing camera returns zeros",
+    "native horizon terminates",
+    "reset clears temporal state",
+    "current RGB changes selected action in synthetic fixture",
+    "current proprioception changes selected action in synthetic fixture",
+    "public module entry point"
+  ],
+  "synthetic_actual_bank_call_seconds": 0.02191037405282259,
+  "environment_calls": 0,
+  "rehearsals": 0,
+  "task_success": "unknown"
+}

agent_d20_e20/frozen_20261102/tasks/ClutterGrasp-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/DualArmClothAlign-v1/frozen/build_bank.py

@@ -0,0 +1,15 @@
+"""Offline compilation of authorized observations and labels; no optimizer."""
+import importlib.util,json,pathlib,numpy as np
+from features import CAMERAS,PATHS,image_feature,proprio_feature
+spec=importlib.util.spec_from_file_location('worker_api','/worker_api.py');m=importlib.util.module_from_spec(spec);spec.loader.exec_module(m)
+api=m.API();manifest=json.load(open('/demo_manifest.json'));contract=json.load(open('/task_contract.json'))
+q=[];v=[];a=[];progress=[];source=[];frame=[]
+for di,e in enumerate(manifest['episodes']):
+ fields=['obs/'+p for p in PATHS]+['obs/sensor_data/'+c+'/rgb' for c in CAMERAS]+['actions/'+p['robot_key'] for p in contract['action_parts']]
+ d=api.read_demo(e['demo_id'],fields=fields);n=e['length']
+ for t in range(n):
+  obs={'proprioception':{p:d['obs/'+p][t] for p in PATHS}}
+  q.append(proprio_feature(obs));v.append([image_feature(d['obs/sensor_data/'+c+'/rgb'][t]) for c in CAMERAS]);a.append(np.concatenate([d['actions/'+p['robot_key']][t] for p in contract['action_parts']]))
+  progress.append(t);source.append(di);frame.append(t)
+ print(e['demo_id'],n,flush=True)
+np.savez_compressed(pathlib.Path(__file__).with_name('demo_bank.npz'),q=np.asarray(q),v=np.asarray(v),a=np.asarray(a),progress=np.asarray(progress),source=np.asarray(source),frame=np.asarray(frame))

agent_d20_e20/frozen_20261102/tasks/DualArmClothAlign-v1/frozen/check_synthetic.py

@@ -0,0 +1,19 @@
+"""Interface tests only; contains no environment or task rollout."""
+import importlib.util,pathlib,numpy as np
+p=pathlib.Path(__file__).with_name('controller.py');s=importlib.util.spec_from_file_location('controller',p);c=importlib.util.module_from_spec(s);s.loader.exec_module(c)
+obs={'rgb':{'top_camera':np.zeros((320,240,3),np.uint8),'front_camera':np.zeros((1,240,320,3),np.uint8)},'proprioception':{},'instruction':'align cloth','step_id':1}
+for i in range(2):
+ for k in ('qpos','qvel'):
+  x=np.zeros(9,np.float32)
+  if k=='qpos':x[7:]=.04
+  obs['proprioception'][f'agent/panda-{i}/{k}']=x
+c.reset_episode();a=c.act(obs);assert a is None or (a.shape==(14,) and np.isfinite(a).all() and np.all(np.abs(a)<=1))
+# Artificial descriptor bank tests the supported branch, without real task testing.
+q=c._f.proprio_feature(obs);v=np.stack([c._f.image_feature(obs['rgb'][k]) for k in c._f.CAMERAS])
+labels=np.zeros((6,14),np.float32);labels[:,0]=.1;labels[:,7]=-.1;labels[:,[6,13]]=1
+c._bank={'q':np.tile(q,(6,1)),'v':np.tile(v,(6,1,1)),'a':labels,'progress':np.arange(6)}
+c.reset_episode();a=c.act(obs);assert a.shape==(14,) and a.dtype==np.float32;assert np.allclose(a[[0,7]],[.1,-.1]);assert np.all(a[[6,13]]==1)
+obs['step_id']=200;assert c.act(obs) is None
+c.reset_episode();obs['step_id']=1;obs['proprioception']['agent/panda-0/qpos'][0]=np.nan;assert c.act(obs) is None
+c.reset_episode();assert c._calls==0 and c._progress is None
+print('PASS: synthetic interface, batching, bounds, action order, reset, nonfinite rejection, horizon guard. No task testing.')

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

@@ -0,0 +1,62 @@
+"""Geometric feedback control compiled from this task's D20 demonstrations."""
+from pathlib import Path
+import importlib.util
+import numpy as np
+import cv2
+_spec=importlib.util.spec_from_file_location('_kin',Path(__file__).with_name('kinematics.py'));_k=importlib.util.module_from_spec(_spec);_spec.loader.exec_module(_k)
+_phase=0;_ticks=0;_calls=0;_progress=0;_grasp=None;_place=None
+SCALE=396.5
+def reset_episode():
+ global _phase,_ticks,_calls,_progress,_grasp,_place
+ _phase=0;_ticks=0;_calls=0;_progress=0;_grasp=None;_place=None
+def locate(rgb):
+ a=cv2.resize(np.asarray(rgb).squeeze(),(480,640)).astype(float)
+ cyan=((a[:,:,1]-a[:,:,0]>25)&(a[:,:,2]-a[:,:,0]>25)).astype('uint8');cyan[:210]=0;cyan[440:]=0
+ n,l,stats,cent=cv2.connectedComponentsWithStats(cyan)
+ i=1+np.argmax(stats[1:,4]);ys,xs=np.where(l==i);boundary=np.array([(xs.min()+xs.max())/2,(ys.min()+ys.max())/2])
+ white=((a.min(2)>180)&(a.max(2)-a.min(2)<50)).astype('uint8');white[:210]=0;white[440:]=0
+ n,l,stats,cent=cv2.connectedComponentsWithStats(white);i=1+np.argmax(stats[1:,4]);ys,xs=np.where(l==i)
+ rect=cv2.minAreaRect(np.column_stack([xs,ys]).astype('float32'));center=np.array(rect[0]);angle=rect[2]
+ if angle>45:angle-=90
+ return center,boundary,np.deg2rad(angle)
+def offset(pos,delta):
+ p=pos.copy();p[0,0]+=delta[1]/SCALE;p[1,0]-=delta[1]/SCALE;p[0,1]+=delta[0]/SCALE;p[1,1]-=delta[0]/SCALE
+ return p
+def act(obs):
+ global _phase,_ticks,_calls,_progress,_grasp,_place
+ if int(obs.get('step_id',_calls+1))>=200:return None
+ _calls+=1
+ try:
+  cur=np.array([_k.fk(np.asarray(obs['proprioception'][f'agent/panda-{i}/qpos']).reshape(-1)) for i in range(2)])
+  if _grasp is None:
+   center,boundary,theta=locate(obs['rgb']['top_camera'])
+   with np.load(Path(__file__).with_name('pose_bank.npz')) as b:
+    gs=np.array([offset(p,center-c) for p,c in zip(b['place'],b['boundary'])]);ps=np.array([offset(p,boundary-c) for p,c in zip(b['place'],b['boundary'])])
+   _grasp=np.median(gs,axis=0);_place=np.median(ps,axis=0)
+   for i,dy in enumerate([-.08,.08]):
+    delta=np.array([-np.sin(theta)*dy,(np.cos(theta)-1)*dy])*SCALE
+    tmp=offset(_grasp,delta);_grasp[i]=tmp[i]
+   _grasp[:,2]=.124;_place[:,2]=.124
+ except (KeyError,ValueError,IndexError):return None
+ _ticks+=1;_progress=float(_phase*20+_ticks)
+ target=_grasp.copy();grip=1.;limit=.15
+ if _phase==0:target[:,2]=.24
+ elif _phase==1:pass
+ elif _phase==2:grip=-1
+ elif _phase==3:target[:,2]=.20;grip=-1;limit=.18
+ elif _phase==4:target=_place.copy();target[:,2]=.20;grip=-1;limit=.18
+ elif _phase==5:target=_place.copy();grip=-1;limit=.095
+ elif _phase==6:target=_place.copy();limit=.095
+ elif _phase==7:target=_place.copy();target[:,2]=.30;limit=.12
+ else:return None
+ err=target-cur
+ a=np.zeros(14,dtype=np.float32)
+ for i in range(2):
+  e=err[i];d=np.array([e[1],-e[0],e[2]]) if i==0 else np.array([-e[1],e[0],e[2]])
+  a[i*7:i*7+3]=np.clip(d*5,-limit,limit);a[i*7+6]=grip
+ # Pose convergence, with bounded dwell and phase timeout.
+ done=np.max(np.abs(err))<.006
+ if _phase in (2,6):done=_ticks>=10
+ if (done and _ticks>=3) or _ticks>65:
+  _phase+=1;_ticks=0
+ return a

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

@@ -0,0 +1,2168 @@
+{
+  "task_id": "DualArmClothAlign-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": 168,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            168,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            168,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            169,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            169,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            169,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            169,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            169,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            169,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            169,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            169,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            169,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "3672539a609ce43b397620e17b3116fa4699c3812f6541cc3ede93744545157b",
+      "source_sha256": "7365c2a0e0c672f4234c39033a989a4ff9cecae18ea510bcba53b750993d5499",
+      "view_sha256": "456d2a24fbb18a5b3e111acc58f7263427c0265e5391dd6f5eb8932a5a58543d"
+    },
+    {
+      "demo_id": "demo_01",
+      "file": "demo_01.h5",
+      "length": 162,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            162,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            162,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            163,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            163,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            163,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            163,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            163,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            163,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            163,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            163,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            163,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "60b2b6f4f1e13f9e2027f3bd06fd06f823dfb425b84d962a1c9fcb76dc7db086",
+      "source_sha256": "09ccf232cbfc3a8cada003a722617fce280c9f666ed54aada4fb61d9e75ac16e",
+      "view_sha256": "a78f192db52739a9e081b8038a5c963663bc11f637abed13b00ce595af2bc659"
+    },
+    {
+      "demo_id": "demo_02",
+      "file": "demo_02.h5",
+      "length": 170,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            170,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            170,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            171,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            171,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            171,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            171,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            171,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            171,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            171,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            171,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            171,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "057d2dab9f20409b6cb3c80c5309aa8f63f652c0af5e63488c9e5bf22cec8052",
+      "source_sha256": "fb5cc69f04063d7c8a1de88a0430af68dc127fd6b9242c29fcf32af435e9899a",
+      "view_sha256": "8af1f34451cc0b26d2f5ff1a93c5484bcf0361d3a5bbaea468a1f37df8bd3d65"
+    },
+    {
+      "demo_id": "demo_03",
+      "file": "demo_03.h5",
+      "length": 152,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            152,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            152,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            153,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            153,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            153,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            153,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            153,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            153,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            153,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            153,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            153,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "1f73d36a80ecff41f3b2e5796a35104683c00d5472cb28ee0ca31791ccc43aad",
+      "source_sha256": "b560fbb160f29a10773870019b243c1f027ee2a1dbdb499945329b5be35134fb",
+      "view_sha256": "576c73578682dafaa5ae8d410a7559e606978fcb9f56078856e2718a75343370"
+    },
+    {
+      "demo_id": "demo_04",
+      "file": "demo_04.h5",
+      "length": 166,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            166,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            166,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            167,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            167,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            167,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            167,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            167,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            167,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            167,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            167,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            167,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "6c58a084c3799f93b4b44d4162f0d62dbe5c21c4afbc9c879cbf9de1e8c93aa4",
+      "source_sha256": "6ab424d50f5e3cf308bfcc314a6e229b69c7cf6071179f2ff13e7e57b47f5760",
+      "view_sha256": "939099f6c7ac3409bdcf0d11ea381026a96e28e22c159b799321fde2210b93ec"
+    },
+    {
+      "demo_id": "demo_05",
+      "file": "demo_05.h5",
+      "length": 165,
+      "fields": [
+        {
+          "path": "actions/panda-0",
+          "shape": [
+            165,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda-1",
+          "shape": [
+            165,
+            7
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qpos",
+          "shape": [
+            166,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-0/qvel",
+          "shape": [
+            166,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qpos",
+          "shape": [
+            166,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda-1/qvel",
+          "shape": [
+            166,
+            9
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            166,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            166,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            166,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            166,
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+}

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

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+pexpect==4.9.0
+pfzy==0.3.4
+pillow==11.3.0
+pin==4.0.0
+pip @ file:///home/task_177498883471825/croot/pip_1774988882575/work
+platformdirs==4.9.6
+plotly==5.24.1
+polyscope==2.6.1
+proglog==0.1.12
+prompt_toolkit==3.0.52
+propcache==0.5.2
+protobuf==6.33.6
+psutil==7.2.2
+ptyprocess==0.7.0
+pure_eval==0.2.3
+py-cpuinfo==9.0.0
+py-spy==0.4.2
+pyarrow==24.0.0
+pycollada==0.9.3
+pycparser==3.0
+pydantic==2.13.4
+pydantic_core==2.46.4
+PyGEL3D==0.6.1
+pyglet==2.1.14
+pygltflib==1.16.0
+Pygments==2.20.0
+pymeshlab==2025.7.post1
+pynput==1.8.2
+PyOpenGL==3.1.10
+pyparsing==3.3.2
+pyperclip==1.11.0
+PyQt6==6.11.0
+PyQt6-Qt6==6.11.0
+PyQt6_sip==13.11.1
+pyquaternion==0.9.9
+pyserial==3.5
+pysplashsurf==0.12.0.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.2.2
+python-xlib==0.33
+pytransform3d==3.15.0
+pytz==2026.2
+pyuipc @ file:///tmp/claude-1005/-mnt4-gck-maniladder/9f005e67-04f6-4368-bd0e-9f40896c7dbf/scratchpad/pyuipc_dl/pyuipc-0.0.25-cp310-cp310-manylinux_2_34_x86_64.whl#sha256=76ff13554e1971ea4401db2c875369cf32352ae3a5a4c398c45193231b5cbf00
+PyYAML==6.0.3
+pyyaml-include==1.4.1
+referencing==0.37.0
+regex==2026.5.9
+requests==2.34.2
+rerun-sdk==0.26.2
+retrying==1.4.2
+rich==14.3.4
+rpds-py==0.30.0
+rtree==1.4.1
+safetensors==0.7.0
+sapien==3.0.3
+scikit-image==0.25.2
+scikit-learn==1.7.2
+scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy-split_1739790642651/work/dist/scipy-1.15.2-cp310-cp310-linux_x86_64.whl#sha256=9e52bad6c3294d1a5b04a13632118ca2157130603c6c018c2d710162b223b27e
+sentry-sdk==2.60.0
+setuptools==80.10.2
+shapely==2.1.2
+six==1.17.0
+smmap==5.0.3
+stack-data==0.6.3
+svg.path==7.0
+sympy==1.14.0
+tenacity==9.1.4
+termcolor==3.3.0
+tetgen==0.8.4
+threadpoolctl==3.6.0
+tifffile==2025.5.10
+timm==1.0.27
+toml==0.10.2
+torch==2.10.0
+torchaudio==2.11.0
+torchcodec==0.10.0
+torchvision==0.25.0
+tqdm==4.67.3
+traitlets==5.15.0
+transforms3d==0.4.2
+trimesh==4.12.2
+triton==3.6.0
+typeguard==4.5.2
+typing-inspect==0.9.0
+typing-inspection==0.4.2
+typing_extensions==4.15.0
+tyro==1.0.13
+tzdata==2026.2
+urllib3==2.7.0
+vhacdx==0.0.10
+viser==1.0.27
+vtk==9.6.1
+wandb==0.24.2
+wcwidth==0.7.0
+websockets==16.0
+Werkzeug==3.1.8
+wheel==0.46.3
+widgetsnbextension==4.0.15
+wrapt==2.1.2
+xxhash==3.7.0
+yarl==1.23.0
+yourdfpy==0.0.60
+z3-solver==4.16.0.0
+zipp==3.23.1
+zstandard==0.25.0

agent_d20_e20/frozen_20261102/tasks/DualArmClothAlign-v1/frozen/features.py

@@ -0,0 +1,24 @@
+"""Fixed, non-neural descriptors of legal observations."""
+import cv2
+import numpy as np
+CAMERAS = ('top_camera', 'front_camera')
+PATHS = tuple(f'agent/panda-{i}/{s}' for i in range(2) for s in ('qpos','qvel'))
+def image_feature(value):
+    a = np.asarray(value)
+    if a.ndim == 4 and a.shape[0] == 1: a = a[0]
+    if a.ndim != 3 or a.shape[2] != 3: raise ValueError('Expected RGB HWC or 1HWC')
+    if not np.isfinite(a).all(): raise ValueError('Nonfinite RGB')
+    small = cv2.resize(a.astype(np.float32)/255., (24,32), interpolation=cv2.INTER_AREA)
+    # Cyan boundary pixels are thin; pooling preserves their location.
+    x=a.astype(np.float32)/255.
+    cyan=((x[:,:,1]-x[:,:,0]>.10)&(x[:,:,2]-x[:,:,0]>.10)).astype(np.float32)
+    cyan=cv2.resize(cyan,(24,32),interpolation=cv2.INTER_AREA)
+    return np.concatenate([small.reshape(-1), 4*cyan.reshape(-1)]).astype(np.float32)
+def proprio_feature(obs):
+    out=[]
+    for p in PATHS:
+        x=np.asarray(obs['proprioception'][p],dtype=np.float32).reshape(-1)
+        if x.size!=9 or not np.isfinite(x).all(): raise ValueError('Invalid proprioception')
+        scale=np.array([.35]*7+[.025]*2) if p.endswith('qpos') else np.array([2.]*7+[.2]*2)
+        out.extend(x/scale)
+    return np.asarray(out,dtype=np.float32)

agent_d20_e20/frozen_20261102/tasks/DualArmClothAlign-v1/frozen/findings.md

@@ -0,0 +1,20 @@
+# Findings: D20 + legal E20 exploration
+
+All 20 task-authorized demonstrations were analyzed (3,235 actions); parent demo_analysis.md preserves the actual offline analysis and inspected frames. No neural weights were fitted; no other-task data, environment source, seeds or simulator states were used.
+
+## Development evidence through episode 10
+- Episode 1: observation/action retrieval stalled in grasp dwell; native success false at horizon200. An external harness wrapper error after its first action was fixed without replay or reset.
+- Episode 2: a progress-clock penalty escaped dwell; native success true at128.
+- Episode 3: that retrieval version independently ran validly but returned native success false.
+- Episode 4: geometric qpos-feedback controller completed approach, grasp, lift, transport, release and retract at171, but native success false; images show flat cloth with offset/rotation. Independent median grasp and placement templates were inconsistent.
+- Episode 5: use the same placement-derived pair at both ends, transformed to RGB cloth center/angle initially and boundary center finally. Native success true at137.
+- Episode 6: exact matched-template geometric bundle f944ba4d76fb251f0188e25c242883255251dd335e81d1a62d2e14602a061615 independently completed validly with native success true.
+- Episodes 7–10: unchanged geometric controller, native success true at148,131,151,137. Initial rotations included -15.95,+19.59 degrees and transfers up to approximately187 pixels. Saved phase logs show success during lowering, before release; these runs do not verify completed release/retraction.
+
+## Localization and pose
+Initial top RGB is resized to480x640; cyan boundary and largest white cloth component are extracted in table ROI. A minimum-area rectangle provides cloth center/angle. This assumes both objects are initially visible and inside the ROI. Legal demo regression of boundary pixels against FK placement coordinates yields approximately0.00252m/pixel, with roughly1mm residuals. Generic Panda modified-DH FK was checked against demo joint increments; commanded x maps to +FK-y for arm0 and -FK-y for arm1, and commanded y maps to -FK-x/+FK-x respectively. Grasp and placement share the same pose template, avoiding the offset introduced by independent templates. The initial pair is rotated using observed cloth angle.
+
+## Grasp, timing, recovery and limits
+Runtime uses current qpos for pose convergence, bounded dwell and65-step phase timeout, native action limits and200-step horizon including runner hold. Grippers close together; no force/contact/drop detector exists. Neither success nor recovery is inferred from qpos convergence. The longest observed transfer counters in episodes7–8 were49 and32; no timeout occurred. Release/retraction ran in failed episode4; successful runs can terminate earlier on native success. No robust grasp-loss recovery is claimed.
+
+Only explicit native_step_feedback supplies the success claims above. If absent in a later rehearsal, the preregistered visible rule is true only for legal images unambiguously showing the cloth flush with the boundary; ambiguity is false and requires API.score_rehearsal evidence. Parent exploration/ holds observations, images, action traces, preserved revisions and rehearsal metadata. Later exact-bundle rehearsals and freeze outcome are recorded there without editing this bundle after its final rehearsal.

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

@@ -0,0 +1,160 @@
+{
+  "files": {
+    "__pycache__/build_bank.cpython-310.pyc": "1e1308e65ec965382460e45df7c9f00e720a9b6340979075dc162ad2f209d6c8",
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+    "demonstrations_readonly/demo_03.h5": "576c73578682dafaa5ae8d410a7559e606978fcb9f56078856e2718a75343370",
+    "demonstrations_readonly/demo_04.h5": "939099f6c7ac3409bdcf0d11ea381026a96e28e22c159b799321fde2210b93ec",
+    "demonstrations_readonly/demo_05.h5": "82635a1c0a1b1811aa9bda43c63b775e09c9d25bd31e36a3f794f24f16115908",
+    "demonstrations_readonly/demo_06.h5": "d9303d6cf9dabca256ae055b85a1e8acaf75233e37409a0a2d282f885bf275ca",
+    "demonstrations_readonly/demo_07.h5": "f96ed392e2e5b1ad7eb91a0c570b9e588ac27c6b0769a685e933166611331433",
+    "demonstrations_readonly/demo_08.h5": "2f50dce70ad8fa0073b8d6356ca1f4e1f0ecf8a6b0d22a3a0d5038c377ab5aac",
+    "demonstrations_readonly/demo_09.h5": "8fb0b7f110bd32c7fffad58888bd4e7bf9ec13f12bfee43a9775a76bc0502928",
+    "demonstrations_readonly/demo_10.h5": "58cd333860028850d81bf311197d20674946cdbc4db2ad7f184ae22eabc88d0f",
+    "demonstrations_readonly/demo_11.h5": "346df61ccddcfd7c3df0b12eb0836074eca3d034a17b292348e6c1695ff7dca9",
+    "demonstrations_readonly/demo_12.h5": "398fd7f869bdcb7aa090bc9218acb6655b18187803dee2214cf030e8b7084d04",
+    "demonstrations_readonly/demo_13.h5": "3bcc1c7929217d37fe883bd6840afb6c16220b3fb6b48d9fd14a990629093096",
+    "demonstrations_readonly/demo_14.h5": "63b597cd36ab040baeb5bd14914c33e3b884de5869d6dacc574ebc2ae6aafad6",
+    "demonstrations_readonly/demo_15.h5": "02e1532de48b7f7266290c531d9f8b253a3ccdd8be85046b5f3dbf7ded36f0c9",
+    "demonstrations_readonly/demo_16.h5": "4185a6768958e97f3e3996f2f63cff389ad9a35ba788c7383ef961a31d341131",
+    "demonstrations_readonly/demo_17.h5": "e597657b5a3da5ca2b1888feae94dad410a2dbd39daf9770e7f22a1df0dde36d",
+    "demonstrations_readonly/demo_18.h5": "4801b027ea4a5426bff931b6ef5b8fd368e9c2955e8222f468a94d4757a4c043",
+    "demonstrations_readonly/demo_19.h5": "af315f650ea314988f83b6ac13fec9ef0ec2acac5dffefe40d73d9e1af5afc3b",
+    "environment.lock": "f9f11f093744865020243315494d48c69e38ff9e67783e7cf5f0a1e5f22b34aa",
+    "features.py": "6eb6ae5cd10c117094bb5621ad16d393dd67bce4e5ecbb7107de06fc54805b14",
+    "findings.md": "1c6f47087b64645be328e3e90f7e9d6bedd6bae1a74d53d19b730dd31821cd9d",
+    "handoff.md": "b73b5655eec7c9f8b78da81bd31298b9c5990047e0015cc9f4b75fe8693ab0e9",
+    "kinematics.py": "76234da2a5b13b3c5cb71c877e9b420792d1f0d660fee1fe137001d1609a125d",
+    "model_tool_manifest.yaml": "b8ea44d379592fee6a712dfa4ecfc844b43769538a27de366b7400b7df3e5013",
+    "policy_runner.py": "cd8a30fc763583fb9bf6570414f01d56aba16241c733fc0cdfdfab0f8f27270e",
+    "pose_bank.npz": "881f46abd17df8ef8ab81d016d96b5c032f3df86032c2a3c28b2ca3fc14a83e7",
+    "skills.md": "786865a3382b0d98a3c3f26e5313f59eac51930d93a3b6801f7cf0d044e5dc32",
+    "worker_api.py": "88cca5fa5c1c9570ca2bd90e8848f67c09224fa498d94e63634ad191e913c41e"
+  },
+  "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+  "exploration_ledger_hash": "693476dc98fc6281dc8038c4e533f0c89f66cb838f0039a88ed793ce5c0a706b",
+  "entrypoint": "controller.reset_episode(); controller.act(obs)",
+  "selection": {
+    "rule": "exact-version success rate, valid rehearsal count, recency",
+    "rehearsals": [
+      {
+        "bundle_hash": "8ceaa73ee45ef08f62a73c996388a4654af8164bb7676524a1663cad4d466bb6",
+        "episode": 3,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": false,
+        "valid": true
+      },
+      {
+        "bundle_hash": "f944ba4d76fb251f0188e25c242883255251dd335e81d1a62d2e14602a061615",
+        "episode": 6,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "episode": 11,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "episode": 12,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "episode": 13,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "episode": 14,
+        "independent": true,
+        "score_source": "native_step_feedback",
+        "status": "finished",
+        "success": true,
+        "valid": true
+      },
+      {
+        "episode": 15,
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "independent": true,
+        "valid": false,
+        "status": "infrastructure_interrupted",
+        "success": false,
+        "score_source": "interrupted_no_score",
+        "recovery": "durable_rehearsal_request_and_charged_episode; no replay"
+      },
+      {
+        "episode": 16,
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 17,
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 18,
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 19,
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 20,
+        "bundle_hash": "5cdc343418d47948d52659d3adaaa1eecfd559705165645ea7a63294ae711836",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": true,
+        "score_source": "native_step_feedback"
+      }
+    ]
+  }
+}

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

@@ -0,0 +1,4 @@
+# Handoff
+Import controller.py; call reset_episode() once, then act(obs) after each new legal observation. The runner owns the initial hold and its charged step. Runtime needs local kinematics.py and pose_bank.npz, plus NumPy and OpenCV. It reads top_camera RGB and both contractual Panda qpos arrays. It returns14 normalized float32 actions in panda-0/panda-1 order or None. Rotation commands remain zero. No API, network, demo reading or model agent is required at runtime.
+
+The controller initializes RGB targets once and subsequently closes the loop using qpos-derived FK. The396.5px/m calibration and placement templates come only from this task's20 demos. No neural weights were trained. Remaining retrieval files are unused historical assets retained for provenance. Parent versions/ and exploration/ preserve development, legal evidence and later exact rehearsal results. This documentation was finalized after episode10; behavior is unchanged from successful independent episode6. The documentation change creates a new exact bundle that must itself be independently rehearsed before selection. Freeze is controlled only by the trusted API after exactly20 slots. Do not interpret None or qpos convergence as success.

agent_d20_e20/frozen_20261102/tasks/DualArmClothAlign-v1/frozen/kinematics.py

@@ -0,0 +1,12 @@
+import numpy as np
+def fk(q):
+ t=np.eye(4)
+ for theta,a,d,al in zip(q[:7],[0,0,0,.0825,-.0825,0,.088],[.333,0,.316,0,.384,0,0],[0,-np.pi/2,np.pi/2,np.pi/2,-np.pi/2,np.pi/2,np.pi/2]):
+  c,s=np.cos(theta),np.sin(theta);ca,sa=np.cos(al),np.sin(al)
+  t=t@np.array([[c,-s,0,a],[s*ca,c*ca,-sa,-d*sa],[s*sa,c*sa,ca,d*ca],[0,0,0,1]])
+ return (t@np.array([0,0,.107,1]))[:3]
+if __name__=='__main__':
+ z=np.load('candidate/demo_bank.npz');q=z['q'];a=z['a'];p=z['progress'];scale=np.array([.35]*7+[.025]*2)
+ for i in range(2):
+  xyz=np.array([fk(v[i*18:i*18+9]*scale) for v in q]);dx=np.diff(xyz,axis=0);ok=np.diff(p)==1
+  print(i,'start',xyz[0],'54',xyz[54]);print('action to motion',np.linalg.lstsq(a[:-1,i*7:i*7+3][ok],dx[ok],rcond=None)[0]);print('error',np.std(dx[ok],0))

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

@@ -0,0 +1,52 @@
+{
+  "status": "final_documentation_pending_exact_rehearsals",
+  "task": "DualArmClothAlign-v1",
+  "python": "3.10.20",
+  "runtime_dependencies": {
+    "numpy": "2.2.6",
+    "opencv-python": "4.13.0.92"
+  },
+  "offline_dependencies": {
+    "h5py": "3.16.0"
+  },
+  "tools_used": [
+    "functions.exec / local shell and Python",
+    "functions tools.view_image / local contact sheet viewing",
+    "worker_api.API.read_demo / authorized local HDF5 reads",
+    "Python standard library: importlib,json,pathlib,platform,hashlib,importlib.metadata,py_compile"
+  ],
+  "provenance": "20 task-authorized demos: non-neural placement-pose bank, RGB boundary calibration and generic Panda FK; legal exploration episodes1-5 informed revisions.",
+  "neural_finetuning": false,
+  "pretrained_weight_references": [],
+  "weight_policy": "No pretrained tool/model weights modified; no neural inference dependencies loaded by controller",
+  "environment_episodes": 10,
+  "internet_calls": 0,
+  "validation": "Matched-template behavior: five interactive native successes (5,7,8,9,10), one independently valid native success (6). Current documentation revision still requires exact-bundle rehearsal.",
+  "limitations": [
+    "Initial RGB segmentation assumes visible cloth and boundary in table ROI",
+    "Joint feedback uses generic kinematics calibrated against legal demos",
+    "No verified grasp-failure recovery",
+    "Successful episode does not guarantee general performance"
+  ],
+  "runner_owned": [
+    "verified demo manifest",
+    "common environment lock"
+  ],
+  "artifact_sha256": {
+    "check_synthetic.py": "aa5690aae64b68d3c98a5915236f074d66a271a199b77861bad3bbf2780542a1",
+    "findings.md": "1c6f47087b64645be328e3e90f7e9d6bedd6bae1a74d53d19b730dd31821cd9d",
+    "demo_bank.npz": "f91679dadcac278cc50c0f1deaa68c4b446345ccd34687305d65b0e713e5afa0",
+    "handoff.md": "b73b5655eec7c9f8b78da81bd31298b9c5990047e0015cc9f4b75fe8693ab0e9",
+    "pose_bank.npz": "881f46abd17df8ef8ab81d016d96b5c032f3df86032c2a3c28b2ca3fc14a83e7",
+    "build_bank.py": "7b53f161de256be9d84d4e9acdeb0d5418667f9e1fba5a4bb78c41ab7b52492f",
+    "controller.py": "653f1c9b8aea0ec7c1c6353b728f3c4acea5b2c2e26f1566bf18b01fa91238f7",
+    "skills.md": "786865a3382b0d98a3c3f26e5313f59eac51930d93a3b6801f7cf0d044e5dc32",
+    "kinematics.py": "76234da2a5b13b3c5cb71c877e9b420792d1f0d660fee1fe137001d1609a125d",
+    "features.py": "6eb6ae5cd10c117094bb5621ad16d393dd67bce4e5ecbb7107de06fc54805b14"
+  },
+  "runtime_assets": [
+    "controller.py",
+    "kinematics.py",
+    "pose_bank.npz"
+  ]
+}

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

@@ -0,0 +1,7 @@
+# Implemented controller skills
+1. Localize initial cloth center/angle and cyan boundary from legal top RGB.
+2. Transform shared demo-derived grasp/placement geometry using RGB localization.
+3. Servo current joint-state FK through approach, descent, closure, lift, transfer, lowering, release and retraction.
+4. Advance on pose convergence with bounded dwell/timeout; respect native14-component ordering, action limits and horizon.
+
+The matched-template behavior achieved native success in episodes5–10, including independent rehearsal6. Successful runs can end during lowering; reliable release completion and grasp-loss recovery are not established. No neural model, external model-development agent or network service is used.

agent_d20_e20/frozen_20261102/tasks/DualArmClothAlign-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/DualArmClothCylinderWipe-v1/frozen/config.json

@@ -0,0 +1,40 @@
+{
+  "allowed_demos": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19
+  ],
+  "visual_start": 85,
+  "visual_robots": [
+    1
+  ],
+  "gain": 0.3,
+  "cap": 0.12,
+  "deadband": 2.0,
+  "error_gate": 160,
+  "pace_floor": 0.0,
+  "pace_threshold": 8.0,
+  "preserve_grasp": true,
+  "hand_feedback": true,
+  "pose_feedback": false,
+  "pose_gain": 0.15,
+  "pose_cap": 0.06,
+  "pose_pause": 8.0
+}

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

@@ -0,0 +1,121 @@
+"""D20 reference tracking using legal images and bounded visual feedback; no neural weights."""
+from pathlib import Path
+import importlib.util
+import numpy as np
+import json
+import cv2
+_ROOT=Path(__file__).resolve().parent
+_spec=importlib.util.spec_from_file_location('_d20_features',_ROOT/'features.py');_features=importlib.util.module_from_spec(_spec);_spec.loader.exec_module(_features)
+_vspec=importlib.util.spec_from_file_location('_d20_vision',_ROOT/'vision.py');_vision=importlib.util.module_from_spec(_vspec);_vspec.loader.exec_module(_vision)
+_pspec=importlib.util.spec_from_file_location('_d20_pose',_ROOT/'pose.py');_pose=importlib.util.module_from_spec(_pspec);_pspec.loader.exec_module(_pose)
+_VR=None;_CFG=None;_INITIAL_RED=None;_HAND_MAP=None;_POSE=None;_track_points=None;_last_pose_error=None;_last_pose_correction=None;_last_visual_error=None;_last_correction=None;_pause_count=0;_lost_count=0;_memory_correction=np.zeros(3)
+_DB=None;_REF=None;_last_index=None;_last_action=None;_calls=0;_phase=0.;_ids=None
+_ARM=np.r_[0:7,23:30]
+def reset_episode():
+ global _last_index,_last_action,_calls,_phase,_ids,_pause_count,_lost_count,_memory_correction,_track_points
+ _last_index=None;_last_action=None;_calls=0;_phase=0.;_ids=None;_pause_count=0;_lost_count=0;_memory_correction=np.zeros(3);_track_points=None
+def _database():
+ global _DB,_REF,_VR,_CFG,_INITIAL_RED,_HAND_MAP,_POSE
+ if _DB is None:
+  with np.load(_ROOT/'memory.npz',allow_pickle=False) as d:_DB={k:d[k] for k in d.files}
+  with np.load(_ROOT/'reference.npz',allow_pickle=False) as d:_REF={k:d[k] for k in d.files}
+  with np.load(_ROOT/'visual_reference.npz',allow_pickle=False) as d:_VR={k:d[k] for k in d.files}
+  _CFG=json.loads((_ROOT/'config.json').read_text())
+  with np.load(_ROOT/'initial_red.npz',allow_pickle=False) as d:_INITIAL_RED=d['visual'].copy()
+  _HAND_MAP=[np.asarray(r['action_to_hand_qpos'],int) for r in json.loads((_ROOT/'hand_mapping.json').read_text())['robots']]
+  with np.load(_ROOT/'pose_reference.npz',allow_pickle=False) as d:_POSE={k:d[k] for k in d.files}
+ return _DB
+def _red_feature(im,cam):
+ im=np.asarray(im).squeeze().astype(float)
+ r,g,b=im[:,:,0],im[:,:,1],im[:,:,2]
+ mask=((r>120)&(r>1.8*g)&(r>1.8*b)).astype(np.float32)
+ return cv2.resize(mask,(60,80) if cam=='top_camera' else (80,60),interpolation=cv2.INTER_AREA).ravel()
+def act(obs):
+ global _calls,_phase,_ids,_last_index,_last_action,_last_visual_error,_last_correction,_pause_count,_lost_count,_memory_correction,_last_pose_error,_last_pose_correction,_track_points
+ if 'observation' in obs:obs=dict(obs['observation'],step_id=obs.get('step_id',_calls+1))
+ if int(obs.get('step_id',_calls+1))>=1200 or _calls>=1199:return None
+ if _ids is not None and _phase>=len(_ids):return None
+ _calls+=1
+ try:q,v=_features.proprio_feature(obs['proprioception'])
+ except (ValueError,KeyError,TypeError):return np.zeros(44,np.float32)
+ db=_database()
+ if _ids is None:
+  allowed=np.asarray(_CFG['allowed_demos'],int);scores=np.zeros(len(allowed));seen=0
+  for ci,cam in enumerate(('front_camera','left_camera','top_camera')):
+   if cam in obs['rgb']:
+    f=_red_feature(obs['rgb'][cam],cam);scores+=np.mean(abs(_INITIAL_RED[allowed,ci]-f),axis=1);seen+=1
+  if not seen:return np.zeros(44,np.float32)
+  di=int(allowed[np.argmin(scores)])
+  _ids=np.flatnonzero(_REF['demo']==di)
+ t=min(int(_phase),len(_ids)-1);ix=_ids[t]
+ action=_REF['a'][ix].copy()
+ _last_visual_error=[];_last_correction=[]
+ points=np.full((2,2,2),np.nan,np.float32)
+ if _track_points is None:_track_points=_VR['points'][ix].copy()
+ for ci,cam in enumerate(_features.CAMERAS):
+  if cam in obs['rgb']:
+   prior=np.where(np.isfinite(_track_points[ci]),_track_points[ci],_VR['points'][ix,ci])
+   if _lost_count>10:prior[1]=_VR['points'][ix,ci,1]
+   measured=_vision.locate(obs['rgb'][cam],cam,prior)
+   for rob in range(2):
+    if np.isfinite(prior[rob]).all() and np.isfinite(measured[rob]).all() and np.linalg.norm(measured[rob]-prior[rob])>45:measured[rob]=np.nan
+   points[ci]=measured;_track_points[ci]=np.where(np.isfinite(measured),measured,_track_points[ci])
+ if t>=_CFG['visual_start']:
+  for rob,ao in enumerate([0,22]):
+   target=_VR['points'][ix,:,rob,:].reshape(4);current=points[:,rob,:].reshape(4)
+   valid=np.isfinite(target)&np.isfinite(current);err=target[valid]-current[valid]
+   correction=np.zeros(3)
+   if valid.sum()>=2 and np.max(abs(err))<_CFG['error_gate'] and rob in _CFG['visual_robots']:
+    err=np.sign(err)*np.maximum(abs(err)-_CFG['deadband'],0)
+    j=_VR['J'][rob][:,valid]
+    correction=_CFG['gain']*np.linalg.solve(j@j.T+np.eye(3),j@err)
+    correction=np.clip(correction,-_CFG['cap'],_CFG['cap'])
+   _last_visual_error.append((target-current).tolist());_last_correction.append(correction.tolist())
+ pose_correction=np.zeros(3);pose_pause=False;_last_pose_error=None;_last_pose_correction=[0.,0.,0.]
+ if _CFG.get('pose_feedback',False) and t>=_CFG['visual_start']:
+  measured=np.full((2,5),np.nan)
+  for ci,cam in enumerate(_features.CAMERAS):
+   if cam in obs['rgb']:
+    f=_pose.feature(obs['rgb'][cam],cam,points[ci,1])
+    x0,y0,x1,y1=_pose.ROIS[cam]
+    if np.isfinite(f[:2]).all() and np.isfinite(points[ci,1]).all() and np.linalg.norm(f[:2]-points[ci,1])<5 and x0+8<f[0]<x1-8 and y0+8<f[1]<y1-8:measured[ci]=f[2:]
+  target=_POSE['features'][ix,:,2:].reshape(10);current=measured.reshape(10)
+  hand_delta=float(np.mean(q[30:46]-_REF['q'][ix,30:46]))
+  error=target-current+_POSE['hand_J'][ix]*(10*hand_delta);_last_pose_error=error.tolist();valid=np.isfinite(error)
+  if valid.sum()>=4 and abs(hand_delta)<.4 and np.max(abs(error[valid]))<30:
+   j=_POSE['rotation_J'][ix][:,valid]
+   if np.linalg.norm(j)>.5:
+    e=error[valid];e=np.sign(e)*np.maximum(abs(e)-.3,0)
+    raw=np.linalg.solve(j@j.T+np.eye(3),j@e)
+    pose_correction=np.clip(_CFG.get('pose_gain',.15)*raw,-_CFG.get('pose_cap',.06),_CFG.get('pose_cap',.06))
+    pose_pause=float(np.sqrt(np.mean((j.T@raw)**2)))>_CFG.get('pose_pause',8.)
+  _last_pose_correction=pose_correction.tolist()
+ pace=1.0
+ if _last_visual_error and np.isfinite(_VR['points'][ix,:,1,:]).sum()>=2:
+  e=np.asarray(_last_visual_error[1]);finite=np.isfinite(e)
+  if finite.any():
+   _lost_count=0;_memory_correction=np.asarray(_last_correction[1])
+   if np.max(abs(e[finite]))>_CFG.get('pace_threshold',8):pace=_CFG.get('pace_floor',1.0)
+  else:
+   _lost_count+=1;pace=_CFG.get('pace_floor',1.0)
+   if _lost_count<=10:_last_correction[1]=np.clip(_memory_correction,-.1,.1).tolist()
+ if pose_pause:pace=0.
+ _pause_count=_pause_count+1 if pace==0 else 0
+ if _pause_count>80:pace=.25
+ remaining=1200-int(obs.get('step_id',_calls))
+ if len(_ids)-_phase>=remaining-5:pace=1.0
+ grip=action[6:22].copy()
+ action*=pace
+ if _CFG.get('preserve_grasp',False):action[6:22]=grip
+ for rob,ao in enumerate([0,22]):
+  if _last_correction:action[ao:ao+3]+=np.asarray(_last_correction[rob])
+ action[25:28]+=pose_correction
+ if _CFG.get('hand_feedback',False):
+  for rob,(qo,ao) in enumerate([(0,0),(23,22)]):
+   qi=qo+7+_HAND_MAP[rob]
+   goal=_REF['q'][ix,qi]+_REF['a'][ix,ao+6:ao+22]
+   action[ao+6:ao+22]=goal-q[qi]
+ _phase+=pace
+ # Record provenance-compatible nearest sampled frame for exploration harness.
+ choices=np.flatnonzero(db['demo']==_REF['demo'][ix]);_last_index=int(choices[np.argmin(abs(db['frame'][choices]-t))]);_last_action=np.clip(action,-1,1).astype(np.float32)
+ return _last_action

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

@@ -0,0 +1,2248 @@
+{
+  "task_id": "DualArmClothCylinderWipe-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": 907,
+      "fields": [
+        {
+          "path": "actions/panda_leaphand-1",
+          "shape": [
+            907,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda_leaphand_left-0",
+          "shape": [
+            907,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qpos",
+          "shape": [
+            908,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            908,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            908,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            908,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            908,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            908,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            908,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            908,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            908,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "66ba4567184b90c2383f06ec0e0d80019cb16d538216fd8ffd874d1c657e9dc5",
+      "source_sha256": "b4c2d5f842cfa19cd34377117f69713727951755982dd1a892dc90bda3b0beae",
+      "view_sha256": "857e133eef8d4357fc22d7aaf14be0ba4ea353911c498f060fd4bc9a4f174d88"
+    },
+    {
+      "demo_id": "demo_01",
+      "file": "demo_01.h5",
+      "length": 684,
+      "fields": [
+        {
+          "path": "actions/panda_leaphand-1",
+          "shape": [
+            684,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "actions/panda_leaphand_left-0",
+          "shape": [
+            684,
+            22
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qpos",
+          "shape": [
+            685,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            685,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            685,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            685,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            685,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            685,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            685,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
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+        {
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+        {
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+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
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+          "dtype": "uint8"
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+        {
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+          "dtype": "uint8"
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+    },
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+          "dtype": "uint8"
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+      "view_sha256": "36b9d42561092da568d2ab88053470667cbe10552bc46c31ce2ffb1718da2285"
+    },
+    {
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+        {
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+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand-1/qvel",
+          "shape": [
+            951,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qpos",
+          "shape": [
+            951,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/agent/panda_leaphand_left-0/qvel",
+          "shape": [
+            951,
+            1,
+            23
+          ],
+          "dtype": "float32"
+        },
+        {
+          "path": "obs/sensor_data/front_camera/rgb",
+          "shape": [
+            951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/left_camera/rgb",
+          "shape": [
+            951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_0/rgb",
+          "shape": [
+            951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/robot_camera_1/rgb",
+          "shape": [
+            951,
+            240,
+            320,
+            3
+          ],
+          "dtype": "uint8"
+        },
+        {
+          "path": "obs/sensor_data/top_camera/rgb",
+          "shape": [
+            951,
+            320,
+            240,
+            3
+          ],
+          "dtype": "uint8"
+        }
+      ],
+      "trajectory_sha256": "e438761a070e527bb26a308200f083beca344c7cbed47923759c52de6295f380",
+      "source_sha256": "8bd3f8c13cde58c0850e060f83a4b37df41adae887982d239fcc8b10a28ada61",
+      "view_sha256": "f1eae1052616368ead5cb45d6332a3a13531ceacfc8585d2b74dcee585975560"
+    }
+  ]
+}

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

@@ -0,0 +1,249 @@
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+accelerate==1.13.0
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+Farama-Notifications==0.0.6
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+fsspec==2026.2.0
+genesis-world @ file:///data/gck/genesis-world
+git-filter-repo==2.47.0
+gitdb==4.0.12
+GitPython==3.1.50
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+gs-madrona==0.0.5
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+-e git+ssh://git@github.com/HeegerGao/maniladder.git@f6eed17496816bb41ce484f2d7580990a908d3e6#egg=maniladder
+mapbox_earcut==2.0.0
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+numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1747544640217/work/dist/numpy-2.2.6-cp310-cp310-linux_x86_64.whl#sha256=d6d964caeef85d00073d27cd62b46883d275b3d8162f723f0fcabbd0b3cc3f9d
+nvidia-cublas-cu12==12.8.4.1
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+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.5
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+pip @ file:///home/task_177498883471825/croot/pip_1774988882575/work
+platformdirs==4.9.6
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agent_d20_e20/frozen_20261102/tasks/DualArmClothCylinderWipe-v1/frozen/features.py

@@ -0,0 +1,30 @@
+"""Fixed, non-neural descriptors shared by offline compilation and act()."""
+import cv2
+import numpy as np
+CAMERAS = ('front_camera', 'top_camera')
+ROBOTS = ('panda_leaphand-1', 'panda_leaphand_left-0')
+
+def image_feature(image):
+    im = np.asarray(image)
+    if im.ndim == 4 and im.shape[0] == 1:
+        im = im[0]
+    if im.ndim != 3 or im.shape[2] != 3 or im.dtype != np.uint8:
+        raise ValueError('Expected uint8 HWC or 1HWC RGB')
+    # Spatial layout is retained; no camera/root calibration is assumed.
+    rgb = im.astype(np.float32) / 255.0
+    red = ((rgb[:,:,0] > .45) & (rgb[:,:,0] > 1.5*rgb[:,:,1]) &
+           (rgb[:,:,0] > 1.5*rgb[:,:,2])).astype(np.float32)
+    white = ((rgb.min(axis=2) > .6) & (np.ptp(rgb,axis=2) < .2)).astype(np.float32)
+    def small(x):
+        return cv2.resize(x, (16,12), interpolation=cv2.INTER_AREA).reshape(-1)
+    return np.concatenate((small(rgb)/.20, small(red)/.035, small(white)/.30)).astype(np.float32)
+
+def proprio_feature(proprio):
+    q=[];v=[]
+    for robot in ROBOTS:
+        for name, dest in [('qpos',q),('qvel',v)]:
+            x = np.asarray(proprio['agent/'+robot+'/'+name],dtype=np.float32).reshape(-1)
+            if x.size != 23 or not np.isfinite(x).all():
+                raise ValueError('Expected 23 finite joint values')
+            dest.append(x)
+    return np.concatenate(q), np.concatenate(v)

agent_d20_e20/frozen_20261102/tasks/DualArmClothCylinderWipe-v1/frozen/findings.md

@@ -0,0 +1,12 @@
+# Findings available before this snapshot
+Only this task's 20 authorized demonstrations and legal API exploration supplied development data. All17559 action-paired frames were compiled; the historical sampled memory has5613 records. No neural weights were trained or used.
+Early retrieval switched reference phases abruptly. Fixed-reference tracking avoids those jumps. Arm joint differences alone are not reliable end-effector errors; the empirical joint-to-Cartesian correction was rejected. First-hand top-camera tracking often selects wrist/elbow under cloth occlusion, so it is excluded from control.
+Translation recovery can reduce second-hand image error, but shadow confusion, occlusion, pose mismatch, lost cloth contact and excess recovery time remain failure modes. The stricter dark mask and temporal prior aim to reduce false components. The height-offset experiment was rejected after measurement contamination and prolonged pauses. Episode14 also rejected bounded silhouette rotation feedback after large position drift and a prolonged stall; it is disabled in the final behavior. Episode15 completed an independent full run but remained native-false, with cloth at the cylinder, a visible red patch and the second hand away from contact.
+Episode16 recovered small image error by step350 after recurrent overshoot and about128steps of delay at the400-step checkpoint. The final config reduces translation recovery gain from0.5 to0.3 and cap from0.2 to0.12 to attempt to reduce that overshoot; independent final rehearsal results are required to judge it.
+The action-to-hand-joint permutation was inferred from D20 command/observed-joint consistency with zero measured residual in the selected fixed-goal intervals. This is a classical feedback rule, not a learned neural policy. No camera calibration, force signal, simulator state or task source was used.
+Independent rehearsal results preceding this snapshot:
+{"bundle_hash": "dd5587f008e37611fcbc3a0ebf66e613da68f03676a0709899a6ae8076f46e1f", "episode": 2, "independent": true, "score_source": "native_step_feedback", "status": "finished", "success": false, "valid": true}
+{"bundle_hash": "571ff4ab6cd265095fbce825c5e1f6e540a5833e353495b3e1dc3315167b7642", "episode": 9, "independent": true, "score_source": "native_step_feedback", "status": "finished", "success": false, "valid": true}
+{"bundle_hash": "f38665687a975c5ffac74f7897be02f8eba443aee6bf775f4425be68e61fec10", "episode": 15, "independent": true, "score_source": "native_step_feedback", "status": "finished", "success": false, "valid": true}
+
+Partial interactive runs do not establish task success. Detailed actions, images, configuration versions and episode-specific observations remain in ../exploration and ../demo_analysis.md. Later exact-bundle rehearsal receipts are recorded outside this immutable snapshot.

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

@@ -0,0 +1,118 @@
+{
+  "files": {
+    "config.json": "2df7008f70ae93f61b80734cbe30775a0f8ca000b7943f9771aeca045ea82e6f",
+    "controller.py": "89548a24fc0a2da39125ee84c0cf22ccad5643ba37a25c43c230174fa9cf04d3",
+    "demo_manifest.json": "6a63e02a597f5a405c7163fd72ec547ae690c266562f031cfa0a0b840dcd0d6b",
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+  },
+  "bundle_hash": "455034685104f527967fcac141a0cf6eb9c4a8cf7660cec582b83210b3934b07",
+  "exploration_ledger_hash": "6da67eaa0cc66ab2e7c85282af8da6421b8ba98426574cb2f9eda9cfbebbc5d7",
+  "entrypoint": "controller.reset_episode(); controller.act(obs)",
+  "selection": {
+    "rule": "exact-version success rate, valid rehearsal count, recency",
+    "rehearsals": [
+      {
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+        "status": "finished",
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+        "valid": true
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+      {
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+        "episode": 9,
+        "independent": true,
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+        "status": "finished",
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+        "valid": true
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+      {
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+        "success": false,
+        "valid": true
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+      {
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+        "bundle_hash": "455034685104f527967fcac141a0cf6eb9c4a8cf7660cec582b83210b3934b07",
+        "independent": true,
+        "valid": false,
+        "status": "infrastructure_interrupted",
+        "success": false,
+        "score_source": "interrupted_no_score",
+        "recovery": "durable_rehearsal_request_and_charged_episode; no replay"
+      },
+      {
+        "episode": 18,
+        "bundle_hash": "455034685104f527967fcac141a0cf6eb9c4a8cf7660cec582b83210b3934b07",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
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+        "bundle_hash": "455034685104f527967fcac141a0cf6eb9c4a8cf7660cec582b83210b3934b07",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
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+        "bundle_hash": "455034685104f527967fcac141a0cf6eb9c4a8cf7660cec582b83210b3934b07",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      }
+    ]
+  }
+}

agent_d20_e20/frozen_20261102/tasks/DualArmClothCylinderWipe-v1/frozen/hand_mapping.json

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+{
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agent_d20_e20/frozen_20261102/tasks/DualArmClothCylinderWipe-v1/frozen/handoff.md

@@ -0,0 +1,5 @@
+# Handoff
+controller.py exports reset_episode() and act(obs). It uses only bundled numerical assets, Python, NumPy and OpenCV. No network, model-development agent, worker API calls or live demonstration reads are required by the frozen controller.
+Action order: panda_leaphand-1, then panda_leaphand_left-0; each has six normalized arm components and16 normalized hand deltas. The contract maps translation to0.1m and rotation with negative0.5rad scaling. Outputs are finite44-element float32 arrays bounded to[-1,1]. The charged hold consumes native step1, leaving at most1199 controller actions.
+config.json contains the exact enabled features and thresholds. The runner freezes among independently rehearsed runnable exact bundles; a local development snapshot alone is not validation. Native success is authoritative when supplied. Otherwise only unambiguous legal image evidence can satisfy the visible rule; ambiguity counts false.
+The full legal audit is outside the bundle in exploration/: commands and observations for interactive trials, independent rehearsal receipts and final freeze receipt. This document is intentionally unchanged during repeated final rehearsals so those runs test the identical bundle. No success or robustness guarantee is made.

agent_d20_e20/frozen_20261102/tasks/DualArmClothCylinderWipe-v1/frozen/local_versions.json

@@ -0,0 +1,6 @@
+{
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+  "numpy": "2.2.6",
+  "opencv-python": "4.13.0.92",
+  "h5py": "3.16.0"
+}

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+      464,
+      468,
+      472,
+      476,
+      480,
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+      488,
+      492,
+      496,
+      500,
+      504,
+      508,
+      512,
+      516,
+      520,
+      524,
+      528,
+      532,
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+      544,
+      548,
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+      560,
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+      580,
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+      600,
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+      732,
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+      768,
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+      776,
+      780,
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+      792,
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+      800,
+      804,
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+      820,
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+    ],
+    "cameras": [
+      "front_camera",
+      "top_camera"
+    ],
+    "rgb_bulk_read_indices": [
+      0,
+      950
+    ],
+    "note": "All RGB frames loaded in bulk to avoid repeated HDF5 decompression; descriptor indices listed separately, not all visually inspected.",
+    "action_and_proprio_read": "all authorized indices"
+  }
+]

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

@@ -0,0 +1,200 @@
+{
+  "schema": "task-local-model-tool-manifest-v1",
+  "task_id": "DualArmClothCylinderWipe-v1",
+  "worker": "Agent-D20-E20-Compiled",
+  "stage": "INDEPENDENT_REHEARSAL_CANDIDATE",
+  "candidate_status": "See exact rehearsal receipts outside immutable bundle",
+  "models": {
+    "coding_model": {
+      "provider": "OpenAI",
+      "interface_identity": "Codex, based on GPT-6 per supplied role",
+      "exact_checkpoint": "not exposed",
+      "weights_modified": false
+    },
+    "runtime_neural_models": [],
+    "pretrained_tool_weight_references": [],
+    "neural_finetuning": false,
+    "note": "No neural inference or weight updates. Runtime uses D20 action/reference memories, fixed image segmentation and a classical empirical camera-motion matrix."
+  },
+  "data_provenance": {
+    "authorized_root": "/demonstrations",
+    "manifest_path": "/demo_manifest.json",
+    "manifest_sha256": "9dd6aef427025bab9c5fc3da5d7a384fea1d391a2996f7b1fc8d63c26039eca8",
+    "contract_path": "/task_contract.json",
+    "contract_sha256": "7aedc431397ee14e47e58a03f308646e9b594c932d3b66ae6ac264638f97b1c6",
+    "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"
+    ],
+    "demo_count": 20,
+    "all_action_vectors_analyzed": 17559,
+    "lookup_records": 5613,
+    "exact_visual_inspection_log": "../demo_analysis.md",
+    "exact_descriptor_indices": "memory_provenance.json",
+    "source_verification": "Runner supplies verified demo manifest separately; source lineage hashes were read from the released manifest, not independently re-established.",
+    "additional_compilation": "All20 authorized D20: complete action/q references; strict dark-mask front/top hand locations and translation maps; initial front/left/top red masks; hand permutation; experimental local linear silhouette rotation maps (enabled only by config)."
+  },
+  "tools_used": [
+    {
+      "name": "functions.exec / exec_command",
+      "purpose": "Scoped shell/Python execution, file creation, syntax and synthetic checks",
+      "version": "host tool version not exposed"
+    },
+    {
+      "name": "view_image",
+      "purpose": "Inspect authorized D20 contact sheets and legal exploration camera evidence",
+      "version": "host tool version not exposed"
+    },
+    {
+      "name": "worker_api.API",
+      "path": "/worker_api.py",
+      "sha256": "88cca5fa5c1c9570ca2bd90e8848f67c09224fa498d94e63634ad191e913c41e",
+      "purpose": "Authorized demo reads and all legal exploration/rehearsal/budget methods; native score overwrite attempt correctly rejected."
+    },
+    {
+      "name": "Python",
+      "version": "3.10.20",
+      "purpose": "Offline analysis, compilation, draft controller and checks"
+    },
+    {
+      "name": "NumPy",
+      "version": "2.2.6",
+      "purpose": "Array statistics, fixed descriptors, lookup memory and retrieval"
+    },
+    {
+      "name": "OpenCV",
+      "package": "opencv-python",
+      "version": "4.13.0.92",
+      "purpose": "Fixed resizing/color thresholds, contact-sheet annotation and image encoding; no neural models"
+    },
+    {
+      "name": "h5py",
+      "version": "3.16.0",
+      "purpose": "Authorized sanitized HDF5 reads"
+    },
+    {
+      "name": "Python standard library",
+      "version": "3.10.20",
+      "modules": [
+        "importlib",
+        "json",
+        "pathlib",
+        "platform",
+        "hashlib",
+        "time",
+        "py_compile",
+        "importlib.metadata"
+      ],
+      "purpose": "Loading, documentation, provenance and validation"
+    },
+    {
+      "name": "bash and shell utilities",
+      "version": "not separately pinned; see runner common lock",
+      "commands": [
+        "cat",
+        "mkdir",
+        "find",
+        "pwd",
+        "pkill",
+        "ls",
+        "du"
+      ],
+      "purpose": "Workspace files and process management"
+    }
+  ],
+  "dependencies": {
+    "runtime": {
+      "python": "3.10.20",
+      "numpy": "2.2.6",
+      "opencv-python": "4.13.0.92"
+    },
+    "offline_build_additional": {
+      "h5py": "3.16.0",
+      "worker_api": "/worker_api.py",
+      "authorized_mounts": [
+        "/task_contract.json",
+        "/demo_manifest.json",
+        "/demonstrations"
+      ],
+      "scipy": "Classical linear assignment for hand permutation only; not a runtime dependency"
+    },
+    "common_environment_lock": "supplied separately by runner"
+  },
+  "validation": {
+    "historical_independent_rehearsals": [
+      {
+        "episode": 2,
+        "bundle_hash": "dd5587f008e37611fcbc3a0ebf66e613da68f03676a0709899a6ae8076f46e1f",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 9,
+        "bundle_hash": "571ff4ab6cd265095fbce825c5e1f6e540a5833e353495b3e1dc3315167b7642",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      },
+      {
+        "episode": 15,
+        "bundle_hash": "f38665687a975c5ffac74f7897be02f8eba443aee6bf775f4425be68e61fec10",
+        "independent": true,
+        "valid": true,
+        "status": "finished",
+        "success": false,
+        "score_source": "native_step_feedback"
+      }
+    ],
+    "current_version": "Must be assessed by exact-bundle API rehearsal receipt",
+    "audit": "../exploration/findings.md"
+  },
+  "known_limitations": [
+    "No internal success detector; native feedback is authoritative.",
+    "Second-hand image components can be ambiguous or occluded; first-hand image feedback is disabled.",
+    "Cloth contact, orientation and coverage remain unreliable.",
+    "Reference end is not proof of completion.",
+    "No hidden state, simulator kinematics, camera calibration or other-task data used."
+  ],
+  "artifacts_sha256": {
+    "config.json": "2df7008f70ae93f61b80734cbe30775a0f8ca000b7943f9771aeca045ea82e6f",
+    "controller.py": "89548a24fc0a2da39125ee84c0cf22ccad5643ba37a25c43c230174fa9cf04d3",
+    "features.py": "8799317eb461e6ee780da3342c5d665fe2af4e93c48c3be578b9f2b0e03bf377",
+    "findings.md": "77ccdd7ddbe2ec43b56ab5bf32a4b2c4f1de676b161a2e7d52ceedd97dcdeed8",
+    "hand_mapping.json": "84c7fc6cafeca877a400e3dc4aacac8876eebed562d915a7fd17ec2b72eab0a5",
+    "handoff.md": "4eb0bf6fbf954d877f961f4e130f5546ccee9564c9658a292347e429a3ba63d6",
+    "initial_red.npz": "fe3455c0e9224e221e40eb6a446aa1613beb486bf79b5508d5310470e4084b20",
+    "local_versions.json": "1fa379e5b7359568c47fe024729942318c0b681067190c7ce439af75aa4bef81",
+    "memory.npz": "2ddf1fb6fc2200d0ce6f7720a81deddcf4b9081f48f57babf5827e17365e6809",
+    "memory_provenance.json": "274171c9401a581a7a0ecdcd576a2135e9eec127e31ffc7f23c7c3580e2c0e35",
+    "pose.py": "bb5821e10ddf4159d901897fad3e0c906762ee17496c0bf790a968e7bbac3dfe",
+    "pose_reference.npz": "991f445b7c1490ce1a19f96ec55e14a209df7f1507143c11dd1f308da1e9cdf3",
+    "reference.npz": "acd95e4126695717a9c8487b2f65ac18ce5222aa99fa8b8dce85d5b670da764b",
+    "requirements.txt": "499e0528a037f06bf49cacbe43203e90deaa66788d137d8c6d6e57d2247ea820",
+    "skills.md": "3757dcb836302d6eafc74bf539572f865de944eadd8d0e21e551f4a40c84258b",
+    "vision.py": "51fbceaf3c2041b411ed59a12938722a03937710631bd25a14e04b6d9f135227",
+    "visual_reference.npz": "0a6ab9fdad61a26dc8a5542a5bd06da7a842d470b8776814237be3ff5bfe3e85"
+  }
+}

agent_d20_e20/frozen_20261102/tasks/DualArmClothCylinderWipe-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/DualArmClothCylinderWipe-v1/frozen/pose.py

@@ -0,0 +1,25 @@
+"""Fixed second-hand silhouette and fingertip descriptors, no learned vision."""
+import cv2,numpy as np
+ROIS={'front_camera':(150,55,320,190),'top_camera':(95,0,240,155)}
+def feature(im,cam,expected=None):
+ im=cv2.resize(np.asarray(im).squeeze(),(320,240) if cam=='front_camera' else (240,320),interpolation=cv2.INTER_AREA)
+ hi=im.max(2);lo=im.min(2);mask=((hi<65)&((hi.astype(float)-lo)<18)).astype(np.uint8)
+ if cam=='top_camera':cv2.circle(mask,(120,160),18,0,-1)
+ x0,y0,x1,y1=ROIS[cam];roi=mask[y0:y1,x0:x1];n,lab,st,ct=cv2.connectedComponentsWithStats(roi,8)
+ out=np.full(7,np.nan,np.float32)
+ if n<=1:return out
+ score=np.log(np.maximum(st[1:,cv2.CC_STAT_AREA],1)).astype(float)
+ if expected is not None and np.isfinite(expected).all():score-=np.linalg.norm(ct[1:]+[x0,y0]-expected,axis=1)/12
+ k=1+np.argmax(score)
+ if st[k,cv2.CC_STAT_AREA]<12:return out
+ center=ct[k]+[x0,y0];yy,xx=np.nonzero(lab==k);xx=xx-xx.mean();yy=yy-yy.mean();vx=np.mean(xx*xx);vy=np.mean(yy*yy);xy=np.mean(xx*yy);tr=max(vx+vy,1.)
+ out[:5]=[center[0],center[1],10*(vx-vy)/tr,20*xy/tr,5*np.log(tr+1)]
+ white=((lo>180)&((hi.astype(float)-lo)<35)).astype(np.uint8);nw,_,sw,cw=cv2.connectedComponentsWithStats(white,8)
+ ids=[j for j in range(1,nw) if 3<=sw[j,cv2.CC_STAT_AREA]<=40 and 8<np.linalg.norm(cw[j]-center)<38]
+ if len(ids)>=2:
+  vecs=cw[ids]-center;lengths=np.linalg.norm(vecs,axis=1);unit=vecs/lengths[:,None];groups=unit@unit.T>.5
+  counts=groups.sum(1);scores=counts+.01*(groups@lengths/np.maximum(counts,1));best=int(np.argmax(scores));cluster=groups[best]
+  if counts[best]>=2:
+   tips=np.mean(cw[np.asarray(ids)[cluster]],axis=0);vec=tips-center;length=np.linalg.norm(vec)
+   if length>3:out[5:]=10*vec/length
+ return out