diff --git a/150case_data_enhance_blender_v2_trajectory_refine/README.md b/150case_data_enhance_blender_v2_trajectory_refine/README.md index 9f3051cd9db3b0937d4e2a573acec4c0f16aaeed..e9b058ae8b2fdbe7b13a38877e58f26c5a1e965d 100644 --- a/150case_data_enhance_blender_v2_trajectory_refine/README.md +++ b/150case_data_enhance_blender_v2_trajectory_refine/README.md @@ -1,10 +1,10 @@ # S1 cup stacking — previous filter and trajectory refinement (LeRobot v2.1) -**105 episodes, 142823 frames, 315 videos**. +**106 episodes, 144375 frames, 318 videos**. Target: `starry0929/rssr/150case_data_enhance_blender_v2_trajectory_refine`. Source `150case_data_enhance_blender_v2` is preserved. This selected dataset contains **92 original-clean trajectories from the previous filter** -and **13 newly refined trajectories** (19 in the frozen refined batch). +and **14 newly refined trajectories** (19 in the frozen refined batch). Each physical group + source case appears once. 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a/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/36411c1f42d329a287f415ad61a2caa32ffe40a2710190e056f9cc179cc26f7c.py b/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/36411c1f42d329a287f415ad61a2caa32ffe40a2710190e056f9cc179cc26f7c.py new file mode 100644 index 0000000000000000000000000000000000000000..645e76e4bc51ed899a88b4dac9195700550b5472 --- /dev/null +++ b/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/36411c1f42d329a287f415ad61a2caa32ffe40a2710190e056f9cc179cc26f7c.py @@ -0,0 +1,80 @@ +"""Additive first-grasp quality gate; original task/replay acceptance is required. +Does not edit native files, old rules, physics, HF or render dispatch. +""" +from pathlib import Path +import argparse,importlib.util,json,hashlib,numpy as np +from release_cycle_integrity import evaluate as evaluate_integrity, acquisition_retention + +def sha(p):return hashlib.sha256(Path(p).read_bytes()).hexdigest() + +def audit(path,out): + root=Path('/pfs/yichen/rssr_data_enhance+causal_map/result/sim1-sim2/s1_first_grasp_v1_20261017') + source=root/'audit/audit_native_first_grasp.py' + spec=importlib.util.spec_from_file_location('first_grasp_audit',source);m=importlib.util.module_from_spec(spec);spec.loader.exec_module(m) + out=Path(out);out.mkdir(parents=True,exist_ok=True);m.OUT=out + receipt_path=Path(path);r=json.loads(receipt_path.read_text());a=receipt_path.parent + result=m.process(receipt_path) + strategy=json.loads((a.parents[2]/'strategy.json').read_text()) + implementation=next(Path(f).parent.parent for f in strategy['implementation_sha256'] if f.endswith('/rssr_augment/common.py')) + saved=np.load(out/'cases'/f"{result['group']}_{result['case_id']}_diagnostics.npz");native=np.load(a/'execute/rollout.npz') + integrity=evaluate_integrity(a,native,saved,implementation) + load_dir=out/'exact_load_replay';load_receipt=load_dir/'native_replay_load_receipt.json' + actual_loads=False;binding={};retention={'passed':False,'reason':'actual_solver_loads_required'} + if load_receipt.exists(): + lr=json.loads(load_receipt.read_text());measured=np.load(load_dir/'native_replay_loads.npz') + binding={ + 'native_source_path':Path(lr['native_source']).resolve()==a.resolve(), + 'native_command_sha256':lr['replay']['command_sha256']==sha(a/'issued_robot_commands.npz'), + 'actual_load_clock_equals_native':np.array_equal(measured['time_s'],native['time_s']), + 'actual_load_object_positions_equal_native':np.array_equal(measured['object_position_m'],native['object_position'][:,:2]), + 'actual_load_shape_and_finite':measured['pad_force_n'].shape==(len(native['time_s']),2,2) and measured['object_in_finger_frame_m'].shape==(len(native['time_s']),2,3) and bool(np.isfinite(measured['pad_force_n']).all()) and bool((measured['pad_force_n']>=0).all()) and bool(np.isfinite(measured['object_in_finger_frame_m']).all()), + } + actual_loads=bool(lr['fresh_native_replay'] and lr['force_from_actual_solver_state'] and lr['sample_clock_exact'] and lr['replay']['exact'] and lr['replay']['every_step_hard_trace_exact'] and all(binding.values())) + if actual_loads: + retention=acquisition_retention(measured['time_s'],measured['pad_force_n'],measured['object_in_finger_frame_m'],saved) + packets=[json.loads(p.read_text())['observation'] for p in sorted((a/'decisions').glob('*/packet.json'))] + result['hands']=[m.summarize_hand(measured['time_s'],native['object_position'],measured['pad_force_n'],measured['object_in_finger_frame_m'],native['gripper_opening'],saved['desired_gripper_angle_rad'],saved['event_kind'],saved['event_object'],saved['event_hand'],hi,packets) for hi in range(2)] + result['force_evidence_source']='Fresh exact native replay solver state; no warmstart reconstruction' + result['needs_optimization']=any(h['needs_optimization'] for h in result['hands']) + checks={ + 'actual_solver_loads_from_exact_replay':actual_loads, + 'original_native_full_task_accepted':r['accepted'] is True, + 'native_task_goal':r['native']['task_text_physical_success'] is True, + 'original_all_hard_passed':r['online_every_step_hard_snapshot']['all_hard_passed'] is True, + 'independent_replay_exact':bool((r.get('replay') or {}).get('exact')), + 'independent_every_step_hard_trace_exact':bool((r.get('replay') or {}).get('every_step_hard_trace_exact')), + 'both_first_close_success':all(h.get('first_close_success') for h in result['hands']), + 'exactly_one_close_each':all(h.get('intent_close_count')==1 for h in result['hands']), + 'no_reopens':all(h.get('commanded_reopen_count')==0 for h in result['hands']), + 'no_recovery_before_lift':all(not h.get('recovery_before_first_proof') for h in result['hands']), + 'no_slip':all(not h.get('slip_candidates') for h in result['hands']), + 'no_object_motion_in_gripper_over_15mm':all(not h.get('relative_motion_review') for h in result['hands']), + 'no_contact_loss_over_100ms_before_release':all(not h.get('contact_loss_before_release') for h in result['hands']), + 'whole_episode_physical_cycle_and_legal_release_integrity':integrity['passed'], + 'first_loaded_grasp_retained_before_and_after_25mm_lift':retention['passed'], + } + original=Path('/pfs/yichen/rssr_data_enhance+causal_map/result/sim1-sim2/s1_heightfit_3x1_20261017') + sample=strategy['physical_augmentation']['profiles'][r['case_id']];gi=sample['variant_index'];old=original/f'physical_{gi:02d}/cases'/r['case_id']/'attempt_00' + old_init=json.loads((old/'s1_command_initialization.json').read_text());new_init=json.loads((a/'s1_command_initialization.json').read_text()) + checks['original_exact_scene_sha256']=sha(a/'execute/scene.xml')==sha(old/'execute/scene.xml') + checks['original_exact_initial_joint_values']=new_init['initial_joint_values_rad']==old_init['initial_joint_values_rad'] + old_rollout=np.load(old/'execute/rollout.npz') + checks['original_exact_initial_native_state']=all(np.array_equal(native[k][0],old_rollout[k][0]) for k in ['qpos','qvel','object_position','object_quaternion']) + checks['original_exact_native_task_contract']=json.loads((a/'execute/task_contract.json').read_text())==json.loads((old/'execute/task_contract.json').read_text()) + # Compare persisted arrays as well as the independent replay's boolean receipt. + def arrays_equal(directory,filename): + other=np.load(directory/filename);source=np.load(a/'execute'/filename) + return source.files==other.files and all(np.array_equal(source[k],other[k]) for k in source.files) + checks['persisted_independent_replay_arrays_exact']=bool((a/'replay/rollout.npz').exists() and arrays_equal(a/'replay','rollout.npz') and arrays_equal(a/'replay','immutable_hard_trace.npz')) + checks['persisted_actual_load_replay_arrays_exact']=bool(actual_loads and arrays_equal(load_dir/'replay','rollout.npz') and arrays_equal(load_dir/'replay','immutable_hard_trace.npz')) + old_audit=json.loads((root/f'audit/cases/physical_{gi:02d}_{r["case_id"]}.json').read_text()) + report={'schema':'rssr.optimized_first_grasp_candidate.v2','candidate':str(a),'source':str(old),'physical_index':gi,'case_id':r['case_id'],'checks':checks,'optimization_accepted':all(checks.values()),'before':old_audit,'after':result,'whole_episode_integrity':integrity,'first_loaded_acquisition_retention':retention,'force_evidence_binding':binding,'audit_criteria':m.CRITERIA,'audit_script_sha256':sha(source),'verifier_script_sha256':sha(__file__),'release_integrity_script_sha256':sha(Path(__file__).parent/'release_cycle_integrity.py'),'native_receipt_sha256':sha(receipt_path),'native_rollout_sha256':sha(a/'execute/rollout.npz'),'native_issued_commands_sha256':sha(a/'issued_robot_commands.npz'),'actual_load_receipt_sha256':sha(load_receipt) if load_receipt.exists() else None,'actual_load_data_sha256':sha(load_dir/'native_replay_loads.npz') if (load_dir/'native_replay_loads.npz').exists() else None,'never_published':True} + dest=out/'optimization_receipt.json' + if dest.exists(): + archive=out/'superseded_quality_receipts';archive.mkdir(exist_ok=True);prior=archive/(sha(dest)+'.json') + if not prior.exists():prior.write_bytes(dest.read_bytes()) + tmp=dest.with_suffix('.json.tmp');tmp.write_text(json.dumps(report,indent=2)+'\n');tmp.replace(dest) + print(json.dumps({'candidate':str(a),'optimization_accepted':report['optimization_accepted'],'checks':checks},indent=2)) + return report +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('receipt',type=Path);p.add_argument('out',type=Path);a=p.parse_args();audit(a.receipt,a.out) diff --git a/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/89ede9f42e342164f059f696ac86b94f7789937852a072ad5b1f4d4fd40a619a.py b/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/89ede9f42e342164f059f696ac86b94f7789937852a072ad5b1f4d4fd40a619a.py new file mode 100644 index 0000000000000000000000000000000000000000..6d9607c31e6645a31de46bb607fce7b0b841c259 --- /dev/null +++ b/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/89ede9f42e342164f059f696ac86b94f7789937852a072ad5b1f4d4fd40a619a.py @@ -0,0 +1,263 @@ +"""Read-only first-grasp diagnostics; never execute commands or alter evidence. + +The existing task judge is intentionally unchanged. This adds stricter acquisition +diagnostics, with explicit sampling limits, using saved native states and commands. +""" +import concurrent.futures as cf +import collections +import csv +import hashlib +import json +import os +import sys +from pathlib import Path +import time +import numpy as np +import mujoco + +SOURCE = Path('/pfs/yichen/rssr_data_enhance+causal_map/result/sim1-sim2/s1_heightfit_3x1_20261017') +OUT = Path(__file__).parent +HANDS = ('left', 'right') +OBJECTS = ('cup_left_base', 'cup_right_upper') +CRITERIA = { + 'sampling': 'All saved native rollout states, nominal 100 Hz; no interpolation of contact.', + 'force_reconstruction': 'MuJoCo mj_forward at saved qpos/qvel/ctrl. Solver warmstart is unavailable; forces are diagnostic reconstructions, not exact replay evidence.', + 'distal_mapping': 'Exact same physical distal bodies as native session._observation; do not trust legacy rollout name-only finger_contact_sides.', + 'loaded_pad_minimum_n': 0.0001, + 'bilateral_acquisition_duration_s': 0.1, + 'lift_above_initial_m': 0.025, + 'lift_hold_duration_s': 0.5, + 'maximum_contact_gap_s': 0.1, + 'command_reopen_delta_rad': 0.12, + 'actual_reopen_delta_m': 0.008, + 'command_reclose_delta_rad': 0.06, + 'slip_drop_m': 0.015, + 'relative_object_motion_review_m': 0.015, + 'first_intent': 'First target/hand contact or grasp action, excluding initial SDK settle and prior approach touches.', + 'cycle': 'Persistent contact/grasp/hold actions remain one closure. A new cycle requires explicit target-hand open intent (approach/retreat/release), commanded reopening >=0.12rad accompanied by >=8mm physical jaw opening, followed by a new commanded close >=0.06rad.', + 'first_success': 'First intentional close obtains >=0.1s loaded bilateral distal contact, reaches 25mm lift in same close, and holds lifted/contacted for >=0.5s; no reopening/reapproach before proof.', + 'retention': 'Separately report >0.1s loaded-contact losses or relative object motion before first intentional release; this does not replace the unchanged task acceptance judge.', +} + + +def digest(path): + h = hashlib.sha256() + with open(path, 'rb') as f: + for block in iter(lambda: f.read(1048576), b''): + h.update(block) + return h.hexdigest() + + +def runs(t, mask): + mask = np.asarray(mask, dtype=bool) + edges = np.diff(np.r_[False, mask, False].astype(np.int8)) + dt = float(np.median(np.diff(t))) + return [(int(a), int(b), float(t[b-1]-t[a]+dt)) + for a,b in zip(np.flatnonzero(edges == 1), np.flatnonzero(edges == -1))] + + +def first_run(t, mask, duration): + return next((a for a,b,d in runs(t, mask) if d >= duration-1e-6), None) + + +def summarize_hand(t, obj_position, pad_force, finger_local, opening, cmd_angle, + event_kind, event_obj, event_hand, hi, packets): + obj, hand = OBJECTS[hi], HANDS[hi] + intended = (event_obj == obj) & (event_hand == hand) + acquisition = intended & np.isin(event_kind, ['contact', 'grasp']) + open_intent = intended & np.isin(event_kind, ['approach', 'retreat', 'release']) + indices = np.flatnonzero(acquisition) + if not len(indices): + return {'object':obj,'hand':hand,'status':'no_intentional_acquisition','needs_optimization':True} + start = int(indices[0]); dt = float(np.median(np.diff(t))) + release_indices = np.flatnonzero(intended & (event_kind == 'release') & (np.arange(len(t)) >= start)) + release = int(release_indices[0]) if len(release_indices) else len(t) + end = release + loaded = (pad_force[:, hi] > CRITERIA['loaded_pad_minimum_n']).all(axis=-1) + unilateral = (pad_force[:, hi] > CRITERIA['loaded_pad_minimum_n']).sum(axis=-1) == 1 + lift = obj_position[:, hi, 2] - obj_position[0, hi, 2] + # A genuine reopening must be both commanded and physically observed. + starts=[start]; opens=[]; phase='closed'; peak=cmd_angle[start,hi] + peak_opening=opening[start,hi]; trough=peak; reopen_opening=peak_opening + for i in range(start+1,end): + angle=cmd_angle[i,hi]; aperture=opening[i,hi] + if phase=='closed': + if angle>peak: + peak=angle;peak_opening=aperture + if (open_intent[i] and peak-angle >= CRITERIA['command_reopen_delta_rad'] and + aperture-peak_opening >= CRITERIA['actual_reopen_delta_m']): + opens.append(i);phase='open';trough=angle;reopen_opening=aperture + else: + trough=min(trough,angle);reopen_opening=max(reopen_opening,aperture) + if acquisition[i] and angle-trough>=CRITERIA['command_reclose_delta_rad']: + starts.append(i);phase='closed';peak=angle;peak_opening=aperture + first_end=opens[0] if opens else end + scope=(np.arange(len(t))>=start)&(np.arange(len(t))=CRITERIA['lift_above_initial_m']),CRITERIA['lift_hold_duration_s']) + # Retreat/reapproach after closing and before stable lift is recovery even + # if a software command happened to preserve the jaw opening. + proof_end=lifted if lifted is not None else first_end + recovery=(intended & np.isin(event_kind,['retreat','approach']) & + (np.arange(len(t))>start)&(np.arange(len(t))=.10] + first_success=lifted is not None and not recovery_spans + qualified=first_run(t,loaded&(lift>=CRITERIA['lift_above_initial_m'])& + (np.arange(len(t))>=start)&(np.arange(len(t))=qualified)&(np.arange(len(t))CRITERIA['maximum_contact_gap_s']+1e-6] + for a,b,dur in loss_spans: + before=max(qualified,a-5);after=min(end-1,b+50) + drop=float(np.max(obj_position[before:a+1,hi,2])-np.min(obj_position[a:after+1,hi,2])) + relative=float(np.max(np.linalg.norm(finger_local[a:after+1,hi]-finger_local[before,hi],axis=1))) + if drop>=CRITERIA['slip_drop_m'] and relative>=CRITERIA['relative_object_motion_review_m']: + slip_spans.append({'start_s':float(t[a]),'end_s':float(t[b-1]+dt),'drop_m':drop,'relative_motion_m':relative}) + max_relative_motion=float(np.max(np.linalg.norm(finger_local[qualified:end,hi]-finger_local[qualified,hi],axis=1))) if end>qualified else 0. + # Only native observation data are read from packets; no model requests. + evidence=[] + for po in packets: + if t[start]-.02<=po['time_s']<=(t[end] if endstart else None, + 'first_close_maximum_lift_m':float(np.max(lift[start:first_end])) if first_end>start else None, + 'first_loaded_bilateral_s':float(t[acquired]) if acquired is not None else None, + 'first_25mm_lift_held_500ms_start_s':float(t[lifted]) if lifted is not None else None, + 'first_close_longest_loaded_bilateral_s':max((d for a,b,d in runs(t,loaded&scope)),default=0.), + 'recovery_before_first_proof':[{'start_s':float(t[a]),'end_s':float(t[b-1]+dt)} for a,b,d in recovery_spans], + 'contact_loss_before_release':[{'start_s':float(t[a]),'end_s':float(t[b-1]+dt),'duration_s':d} for a,b,d in loss_spans], + 'slip_candidates':slip_spans,'max_object_motion_in_gripper_frame_m':max_relative_motion, + 'relative_motion_review':max_relative_motion>CRITERIA['relative_object_motion_review_m'], + 'needs_optimization':bool(not first_success or len(starts)>1 or recovery_spans or slip_spans), + 'native_packet_force_evidence':evidence, + } + + +def process(receipt_path): + receipt_path=Path(receipt_path);a=receipt_path.parent;r=json.loads(receipt_path.read_text()) + model=mujoco.MjModel.from_xml_path(str(a/'execute/scene.xml'));data=mujoco.MjData(model) + z=np.load(a/'execute/rollout.npz');t=z['time_s'];q=z['qpos'];v=z['qvel'];ctrl=z['ctrl'];opening=z['gripper_opening'];pos=z['object_position'] + commands=np.load(a/'issued_robot_commands.npz');ct=commands['time_s'];cq=commands['qpos'];idx=np.clip(np.searchsorted(ct,t),0,len(ct)-1) + qa=[int(model.joint(f'astribot_gripper_{h}_joint_L1').qposadr[0]) for h in HANDS];cmd_angle=cq[idx][:,qa] + del cq,commands + history=r['event_history'];ends=np.asarray([e['time_s'] for e in history]);ei=np.minimum(np.searchsorted(ends,t,side='right'),len(history)-1) + event_kind=np.asarray([history[i]['event_id'].split(':')[0] for i in ei]);event_obj=np.asarray([history[i].get('selected_object_id') or '' for i in ei]);event_hand=np.asarray([history[i].get('selected_hand') or '' for i in ei]) + object_bodies={model.body(obj).id:oi for oi,obj in enumerate(OBJECTS)} + distal={};tip_geoms=[];hand_bodies=[] + for hi,h in enumerate(HANDS): + tips=[model.geom(f'astribot_gripper_{h}_geom_{s}').id for s in ('L112','R112')] + tip_geoms.append(tips);hand_bodies.append(model.body(f'astribot_gripper_{h}_base').id) + for side,g in enumerate(tips):distal[int(model.geom_bodyid[g])]=(hi,side) + forces=np.zeros((len(t),2,2));finger_local=np.zeros((len(t),2,3));contact=np.zeros((len(t),2,2),bool) + for k in range(len(t)): + data.qpos[:]=q[k];data.qvel[:]=v[k];data.ctrl[:]=ctrl[k];data.time=t[k];mujoco.mj_forward(model,data) + for hi in range(2): + center=data.geom_xpos[tip_geoms[hi]].mean(axis=0) + rot=data.xmat[hand_bodies[hi]].reshape(3,3) + finger_local[k,hi]=rot.T@(pos[k,hi]-center) + for ci,c in enumerate(data.contact): + if c.dist>0:continue + for geom,other in ((int(c.geom1),int(c.geom2)),(int(c.geom2),int(c.geom1))): + oi=object_bodies.get(int(model.geom_bodyid[geom]));ds=distal.get(int(model.geom_bodyid[other])) + if oi is not None and ds is not None and oi==ds[0]: + hi,side=ds;contact[k,hi,side]=True;f=np.zeros(6);mujoco.mj_contactForce(model,data,ci,f) + forces[k,hi,side]=max(forces[k,hi,side],max(0.,float(f[0]))) + packets=[] + for packet in sorted((a/'decisions').glob('*/packet.json')): + packets.append(json.loads(packet.read_text())['observation']) + diagnostics=[summarize_hand(t,pos,forces,finger_local,opening,cmd_angle,event_kind,event_obj,event_hand,hi,packets) for hi in range(2)] + legacy=np.stack([z['finger_contact_sides'][:,hi,hi,:] for hi in range(2)],axis=1).astype(bool) + result={'group':a.parents[2].name,'case_id':r['case_id'],'accepted':r['accepted'], + 'receipt_path':str(receipt_path),'receipt_sha256':digest(receipt_path), + 'scene_sha256':digest(a/'execute/scene.xml'),'hands':diagnostics, + 'needs_optimization':any(h['needs_optimization'] for h in diagnostics), + 'legacy_finger_side_samples_disagree':int(np.any(contact!=legacy,axis=(1,2)).sum()), + 'rollout_samples':len(t),'task_required_failures':r['native']['required_failures']} + stem=f"{result['group']}_{r['case_id']}" + (OUT/'cases').mkdir(exist_ok=True) + (OUT/'cases'/f'{stem}.json').write_text(json.dumps(result,indent=2)+'\n') + np.savez_compressed(OUT/'cases'/f'{stem}_diagnostics.npz',time_s=t,pad_force_n=forces, + distal_contact=contact,object_in_finger_frame_m=finger_local, + desired_gripper_angle_rad=cmd_angle,event_kind=event_kind, + event_object=event_obj,event_hand=event_hand) + return result + + +def summarize_saved(receipt_path): + """Refine diagnostic criteria using unchanged, previously derived FK/forces.""" + receipt_path=Path(receipt_path);a=receipt_path.parent + stem=f"{a.parents[2].name}_{a.parent.name}" + report_path=OUT/'cases'/f'{stem}.json' + r=json.loads(report_path.read_text()) + assert r['receipt_sha256']==digest(receipt_path) + assert r['scene_sha256']==digest(a/'execute/scene.xml') + z=np.load(OUT/'cases'/f'{stem}_diagnostics.npz') + native=np.load(a/'execute/rollout.npz') + packets=[json.loads(p.read_text())['observation'] + for p in sorted((a/'decisions').glob('*/packet.json'))] + r['hands']=[summarize_hand(z['time_s'],native['object_position'],z['pad_force_n'], + z['object_in_finger_frame_m'],native['gripper_opening'], + z['desired_gripper_angle_rad'],z['event_kind'],z['event_object'], + z['event_hand'],hi,packets) for hi in range(2)] + r['needs_optimization']=any(h['needs_optimization'] for h in r['hands']) + report_path.write_text(json.dumps(r,indent=2)+'\n') + return r + + +def main(): + receipts=sorted(SOURCE.glob('physical_??/cases/*/attempt_00/receipt.json')) + assert len(receipts)==150 + (OUT/'criteria.json').write_text(json.dumps(CRITERIA,indent=2)+'\n') + results=[];errors=[];started=time.monotonic() + with cf.ProcessPoolExecutor(max_workers=6) as pool: + worker=summarize_saved if '--summarize-saved' in sys.argv else process + jobs={pool.submit(worker,str(p)):p for p in receipts} + for future in cf.as_completed(jobs): + try:results.append(future.result()) + except Exception as exc:errors.append({'receipt':str(jobs[future]),'error':repr(exc)}) + if (len(results)+len(errors))%10==0: + print(json.dumps({'done':len(results),'errors':len(errors),'seconds':round(time.monotonic()-started,1)}),flush=True) + results.sort(key=lambda r:(r['group'],r['case_id'])) + rows=[] + for r in results: + for h in r['hands']: + rows.append({'group':r['group'],'case_id':r['case_id'],'accepted':r['accepted'], + **{k:h.get(k) for k in ['hand','status','needs_optimization','first_close_success','first_intent_s','intent_close_count','commanded_reopen_count','first_close_minimum_aperture_m','first_close_maximum_lift_m','first_loaded_bilateral_s','first_25mm_lift_held_500ms_start_s','first_close_longest_loaded_bilateral_s','max_object_motion_in_gripper_frame_m','relative_motion_review']}, + 'contact_loss_count':len(h.get('contact_loss_before_release',[])), + 'slip_candidate_count':len(h.get('slip_candidates',[])), + 'recovery_before_proof_count':len(h.get('recovery_before_first_proof',[])), + 'report_path':str(OUT/'cases'/f"{r['group']}_{r['case_id']}.json")}) + with (OUT/'all_150_hands.csv').open('w',newline='') as f: + writer=csv.DictWriter(f,fieldnames=list(rows[0]));writer.writeheader();writer.writerows(rows) + summary={'schema':'rssr.first_grasp_readonly_audit.v2','source_root':str(SOURCE), + 'cases_expected':150,'cases_audited':len(results),'errors':errors, + 'cases_needing_optimization':sum(r['needs_optimization'] for r in results), + 'accepted_cases_needing_optimization':sum(r['accepted'] and r['needs_optimization'] for r in results), + 'hand_status_counts':dict(collections.Counter(row['status'] for row in rows)), + 'hands_with_multiple_closes':sum((row['intent_close_count'] or 0)>1 for row in rows), + 'hands_with_slip_candidates':sum(row['slip_candidate_count']>0 for row in rows), + 'cases_with_legacy_contact_disagreement':sum(r['legacy_finger_side_samples_disagree']>0 for r in results), + 'wall_seconds':time.monotonic()-started,'criteria_path':str(OUT/'criteria.json'), + 'audit_script_sha256':digest(__file__)} + (OUT/'summary.json').write_text(json.dumps(summary,indent=2)+'\n') + (OUT/'all_150_cases.json').write_text(json.dumps(results,indent=2)+'\n') + print(json.dumps(summary),flush=True) + + +if __name__=='__main__':main() diff --git a/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/c03e452174525918d03adce01336cc649ef9ef5d8dbfa1eb55a6d77a59b58943.py b/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/c03e452174525918d03adce01336cc649ef9ef5d8dbfa1eb55a6d77a59b58943.py new file mode 100644 index 0000000000000000000000000000000000000000..d1d18bb94950a19436c36daf12f95c2953b95e26 --- /dev/null +++ b/150case_data_enhance_blender_v2_trajectory_refine/evidence/episodes/physical_01__stack_cups_ep000016/f11657ca48b0596351c0eff4194fbc1b79711c14b9a51f7a7f638c68829ff809/verifiers/c03e452174525918d03adce01336cc649ef9ef5d8dbfa1eb55a6d77a59b58943.py @@ -0,0 +1,178 @@ +"""Read-only, full-episode close cycles and original-policy release checks. + +Unlike the original screening summary, opening detection does not depend on the +event name and counting continues after the first release. Intentional release +uses the frozen native guard verbatim; its geometry-ready path is legitimate. +""" +from pathlib import Path +import copy +import hashlib +import json +import sys +import numpy as np + + +def acquisition_retention(time_s, forces, local, diagnostics): + """Keep the first stable loaded grasp, including the pre-25mm lift phase.""" + t = time_s; dt = float(np.median(np.diff(t))); rows = [] + for hi, (hand, obj) in enumerate(zip(('left', 'right'), ('cup_left_base', 'cup_right_upper'))): + intended = (diagnostics['event_object'] == obj) & (diagnostics['event_hand'] == hand) + acquisition = np.flatnonzero(intended & np.isin(diagnostics['event_kind'], ['contact', 'grasp'])) + if not len(acquisition): + rows.append({'hand': hand, 'passed': False, 'reason': 'no_acquisition'}); continue + first = int(acquisition[0]) + releases = np.flatnonzero(intended & (diagnostics['event_kind'] == 'release') & (np.arange(len(t)) >= first)) + end = int(releases[0]) if len(releases) else len(t) + loaded = (forces[:, hi] > .0001).all(axis=-1) + def spans(mask): + edges = np.diff(np.r_[False, mask, False].astype(np.int8)) + return [(int(a), int(b), float(t[b - 1] - t[a] + dt)) + for a, b in zip(np.flatnonzero(edges == 1), np.flatnonzero(edges == -1))] + scope = (np.arange(len(t)) >= first) & (np.arange(len(t)) < end) + qualified = next((a for a, b, duration in spans(loaded & scope) if duration >= .1 - 1e-6), None) + losses = []; motion = None + if qualified is not None: + retain = (np.arange(len(t)) >= qualified) & (np.arange(len(t)) < end) + losses = [{'start_s': float(t[a]), 'end_s': float(t[b - 1] + dt), 'duration_s': duration} + for a, b, duration in spans((~loaded) & retain) if duration > .1 + 1e-6] + motion = float(np.max(np.linalg.norm(local[qualified:end, hi] - local[qualified, hi], axis=1))) + rows.append({'hand': hand, 'object': obj, + 'first_stable_loaded_acquisition_s': float(t[qualified]) if qualified is not None else None, + 'contact_loss_after_first_loaded_acquisition': losses, + 'max_object_motion_from_first_loaded_acquisition_m': motion, + 'passed': qualified is not None and not losses and motion <= .015}) + return {'schema': 'rssr.first_loaded_acquisition_retention.v1', + 'scope': 'first 100ms loaded bilateral acquisition until first legal release, including before the 25mm lift threshold', + 'maximum_contact_gap_s': .1, 'maximum_object_motion_in_gripper_m': .015, + 'hands': rows, 'passed': all(r['passed'] for r in rows)} + + +def evaluate(case, native, diagnostics, implementation): + case = Path(case) + sys.path.insert(0, str(implementation)) + from rssr_augment.retarget.events import catalog + from rssr_augment.retarget.immutable_rule_guard import guard + + receipt = json.loads((case / 'receipt.json').read_text()) + contract = json.loads((case / 'execute/task_contract.json').read_text()) + packets = [json.loads(p.read_text()) for p in sorted((case / 'decisions').glob('*/packet.json'))] + events = catalog(contract, {'objects': packets[0]['scene_objects']}) + t = native['time_s'] + kind, obj, hand = [diagnostics[k] for k in ('event_kind', 'event_object', 'event_hand')] + angle, aperture = diagnostics['desired_gripper_angle_rad'], native['gripper_opening'] + rows = [] + for hi, (h, o) in enumerate(zip(('left', 'right'), ('cup_left_base', 'cup_right_upper'))): + intent = (obj == o) & (hand == h) + acquisition = intent & np.isin(kind, ['contact', 'grasp']) + starts = np.flatnonzero(acquisition) + if not len(starts): + rows.append({'hand': h, 'object': o, 'passed': False, 'reason': 'no_acquisition'}) + continue + first = int(starts[0]) + releases = np.flatnonzero(intent & (kind == 'release') & (np.arange(len(t)) >= first)) + release = int(releases[0]) if len(releases) else None + + # First pre-close centering may open an initially half-closed SDK hand. + # A meaningful close starts only after a positive 0.06rad command rise. + state = 'waiting'; trough = float(angle[first, hi]); peak = trough + minimum_aperture = float(aperture[first, hi]); closes = []; opens = [] + for i in range(first + 1, len(t)): + a, gap = float(angle[i, hi]), float(aperture[i, hi]) + if state == 'waiting': + trough = min(trough, a) + if a - trough >= .06 - 1e-12: + closes.append(i); state = 'closing'; peak = a; minimum_aperture = gap + else: + peak = max(peak, a); minimum_aperture = min(minimum_aperture, gap) + if peak - a >= .12 - 1e-12 and gap - minimum_aperture >= .008 - 1e-12: + opens.append(i); state = 'waiting'; trough = a + unplanned = [i for i in opens if release is None or i < release] + later_acquisition = np.flatnonzero(acquisition & (np.arange(len(t)) >= (release if release is not None else len(t)))) + + release_evidence = {'present': release is not None, 'original_guard_allowed': False} + support_ok = False + if release is not None: + # The history rows record action end times. Find the exact action + # start boundary, then its saved native decision observation. + history = receipt['event_history'] + ri = next(i for i, e in enumerate(history) + if e['event_id'].split(':')[0] == 'release' + and e.get('selected_object_id') == o and e.get('selected_hand') == h) + release_start = float(history[ri - 1]['time_s']) if ri else 0. + packet = min(packets, key=lambda p: abs(p['observation']['time_s'] - release_start)) + packet_gap = abs(packet['observation']['time_s'] - release_start) + event = next(e for e in events if e.action.event == 'release' and e.action.object_id == o and e.action.hand == h) + packet_allowed, packet_reasons = guard(event, packet['observation'], contract, events) + # Packets are presentation records rounded to five decimal places. + # Rechecking a 6mm physical guard on them can reject 5.997104mm as + # 6.000000000000005mm. The preceding native history row is the + # actual same action boundary, with unrounded positions/predicates. + # Keep the frozen guard and its thresholds completely unchanged. + boundary = history[ri - 1] if ri else None + trusted = copy.deepcopy(packet['observation']) + boundary_valid = bool(boundary and all(k in boundary for k in + ('object_position_m', 'live_predicates', 'immutable_hard'))) + if boundary_valid: + raw_time = float(boundary['immutable_hard']['time_s']) + boundary_valid = (abs(raw_time - release_start) <= 1e-4 and + abs(raw_time - packet['observation']['time_s']) <= 1e-4 and + all(np.array_equal(np.round(np.asarray(v), 5), + np.asarray(packet['observation']['object_position_m'][k])) + for k, v in boundary['object_position_m'].items())) + for key in ('object_position_m', 'live_predicates', 'finger_contact', + 'hand_contact', 'gripper_opening_m', 'gripper_scene_contacts', + 'immutable_hard'): + if key in boundary: trusted[key] = copy.deepcopy(boundary[key]) + trusted['time_s'] = raw_time + allowed, reasons = guard(event, trusted, contract, events) + if not boundary_valid: + reasons = list(reasons) + ['same_boundary_unrounded_native_evidence_missing_or_mismatched'] + allowed = False + release_evidence.update(time_s=release_start, packet_time_s=packet['observation']['time_s'], + packet_time_error_s=packet_gap, original_guard_allowed=bool(allowed and packet_gap <= 1e-4), + original_guard_reasons=reasons, + guard_input_precision='Unrounded native history action boundary; no epsilon or physical threshold change', + native_history_row_index=ri - 1 if ri else None, + native_history_row_sha256=hashlib.sha256(json.dumps(boundary,sort_keys=True,separators=(',',':')).encode()).hexdigest() if boundary else None, + native_history_exact_time_s=trusted['time_s'], + native_boundary_valid=boundary_valid, + rounded_packet_guard_allowed=packet_allowed, + rounded_packet_guard_reasons=packet_reasons, + support_at_release=packet['observation']['support_contact'][o]) + + # Original stack contract: base must settle on worktable; upper on + # base. contacts[..., -1] is the runtime worktable column. The + # unchanged native final task judge still supplies relation, + # contact-fraction, released and full 1s stability requirements. + supported = (native['contacts'][:, hi, -1] > 0 if hi == 0 + else native['object_contacts'][:, hi, 0] > 0) + stable = next(p for p in contract['predicates'] if p['kind'] == 'stable') + window = float(stable['window_s']); final_window = t >= t[-1] - window - 1e-8 + motion = float(np.linalg.norm(np.ptp(native['object_position'][final_window, hi], axis=0))) + support_ok = bool(np.all(supported[final_window]) and motion <= float(stable['max_motion_m']) + 1e-12) + supported_after = np.flatnonzero(supported & (np.arange(len(t)) >= release)) + release_evidence.update(expected_support='worktable' if hi == 0 else 'cup_left_base', + first_post_release_support_s=float(t[supported_after[0]]) if len(supported_after) else None, + final_support_continuous=bool(np.all(supported[final_window])), + original_stability_window_s=window, final_window_motion_m=motion, + original_maximum_motion_m=float(stable['max_motion_m']), + supported_and_stable_at_end=support_ok) + checks = { + 'one_meaningful_close_in_full_episode': len(closes) == 1, + 'no_unplanned_physical_reopens_in_any_event': not unplanned, + 'no_acquisition_after_first_release': not len(later_acquisition), + 'first_release_allowed_by_original_native_guard': release_evidence['original_guard_allowed'], + 'final_expected_support_and_original_stability': support_ok, + } + rows.append({'hand': h, 'object': o, 'checks': checks, 'passed': all(checks.values()), + 'meaningful_close_s': [float(t[i]) for i in closes], + 'actual_reopen_s': [float(t[i]) for i in opens], + 'unplanned_actual_reopen_s': [float(t[i]) for i in unplanned], + 'post_release_acquisition_samples': len(later_acquisition), + 'release': release_evidence}) + return {'schema': 'rssr.full_episode_grasp_release_integrity.v2', + 'criteria': {'close_command_rise_rad': .06, 'reopen_command_fall_rad': .12, + 'reopen_actual_aperture_rise_m': .008, + 'cycle_window': 'first target acquisition to complete native episode, all event names', + 'release_policy': 'unmodified frozen immutable_rule_guard.guard; support OR exact frozen goal geometry'}, + 'hands': rows, 'passed': all(row['passed'] for row in rows)} diff --git a/150case_data_enhance_blender_v2_trajectory_refine/manifest.json b/150case_data_enhance_blender_v2_trajectory_refine/manifest.json index 70cfdf36d95b59577df1c92ceeea42704e69bdea..c36d090019196d6585c0a83b8a69d579aaa6ce61 100644 --- a/150case_data_enhance_blender_v2_trajectory_refine/manifest.json +++ b/150case_data_enhance_blender_v2_trajectory_refine/manifest.json @@ -4,12 +4,12 @@ "path_in_repo": "150case_data_enhance_blender_v2_trajectory_refine", "dataset_path": ".", "source_prefix_read_only": "150case_data_enhance_blender_v2", - "episodes": 105, - "frames": 142823, - "videos": 315, + "episodes": 106, + "frames": 144375, + "videos": 318, "origin_counts": { "original_clean": 92, - "refined": 13 + "refined": 14 }, "original_previous_filter_count": 92, "refined_snapshot_expected": 19, diff --git a/150case_data_enhance_blender_v2_trajectory_refine/meta/episodes.jsonl b/150case_data_enhance_blender_v2_trajectory_refine/meta/episodes.jsonl index ae9b74ba09462f81c77dc258b4a61c910868076d..a40ac9c0c11398a40cd4f760e917c43efec1bf06 100644 --- a/150case_data_enhance_blender_v2_trajectory_refine/meta/episodes.jsonl +++ b/150case_data_enhance_blender_v2_trajectory_refine/meta/episodes.jsonl @@ -103,3 +103,4 @@ {"episode_index": 102, "tasks": ["Stack_the_cups_together."], "length": 1369} {"episode_index": 103, "tasks": ["Stack_the_cups_together."], "length": 1475} {"episode_index": 104, "tasks": ["Stack_the_cups_together."], "length": 1482} +{"episode_index": 105, "tasks": ["Stack_the_cups_together."], "length": 1552} diff --git a/150case_data_enhance_blender_v2_trajectory_refine/meta/episodes_stats.jsonl b/150case_data_enhance_blender_v2_trajectory_refine/meta/episodes_stats.jsonl index 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