Upload harness_robolab_exploration outputs

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@@ -58,3 +58,54 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval/episode-0001/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval/episode-0002/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval/episode-0003/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval/episode-0004/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval/episode-0007/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval/episode-0008/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/BananasInBinThreeTotalTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/BananasInBinThreeTotalTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/BananasInBinThreeTotalTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/BowlStackingRightOnLeftTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/BowlStackingRightOnLeftTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/BowlStackingRightOnLeftTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/OneBottleInSquarePailTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/OneBottleInSquarePailTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/OneBottleInSquarePailTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/PlasticBottlesInSquarePailTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/PlasticBottlesInSquarePailTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_0/robolab/PlasticBottlesInSquarePailTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0001/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0002/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0003/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0004/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0005/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0006/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0007/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0008/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0010/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0011/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0012/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0013/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/harness/20261030T160311.809521Z_eval/episode-0015/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/BananaThenRubiksCubeTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/BananaThenRubiksCubeTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/BananaThenRubiksCubeTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/BlockStackingSpecifiedOrderTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/BlockStackingSpecifiedOrderTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/BlockStackingSpecifiedOrderTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/FoodPacking1CansTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/FoodPacking1CansTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/FoodPacking1CansTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/FruitsOnionToPlateTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/FruitsOnionToPlateTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/FruitsOnionToPlateTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/ReorientRedMugTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/ReorientRedMugTask/run_1.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_1/robolab/ReorientRedMugTask/run_2.hdf5 filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_2/harness/20261030T160311.809001Z_eval/episode-0001/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_2/harness/20261030T160311.809001Z_eval/episode-0002/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_2/harness/20261030T160311.809001Z_eval/episode-0003/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_2/harness/20261030T160311.809001Z_eval/episode-0004/events.jsonl filter=lfs diff=lfs merge=lfs -text
+harness_robolab_exploration/embo_astra/gpu_2/harness/20261030T160311.809001Z_eval/episode-0005/events.jsonl filter=lfs diff=lfs merge=lfs -text

harness_robolab_exploration/action_format_analysis_20261029/analyze.py

@@ -0,0 +1,167 @@
+from pathlib import Path
+import collections
+import csv
+import hashlib
+import json
+import re
+import statistics as st
+import numpy as np
+
+ROOT = Path('/data/NTU_slab/draven/proj/codebase/outputs/robolab_exploration')
+OUT = ROOT / 'action_format_analysis_20261029'
+OUT.mkdir(exist_ok=True)
+NAMES = ['eef_abs', 'eef_delta', 'joint_abs', 'joint_delta']
+
+def rows(path):
+    with path.open() as stream:
+        for line in stream:
+            if line.strip():
+                yield json.loads(line)
+
+def write_csv(name, data):
+    keys = list(dict.fromkeys(k for row in data for k in row))
+    with (OUT / name).open('w') as stream:
+        w = csv.DictWriter(stream, keys)
+        w.writeheader()
+        w.writerows(data)
+
+def event_stats(path):
+    items, act_lines, seen = [], [], set()
+    result = {}
+    for line, raw in enumerate(path.open(), 1):
+        # Skip image-heavy motion responses and token deltas before decoding.
+        wanted_start = '"item/started"' in raw and '"tool": "act"' in raw
+        wanted_completed = '"item/completed"' in raw and (
+            '"type": "commandExecution"' in raw or '"type": "fileChange"' in raw or
+            ('initial_image_hashes' not in result and '"tool": "observe"' in raw))
+        if not (wanted_start or wanted_completed):
+            continue
+        row = json.loads(raw)
+        e = row.get('event', {})
+        method = e.get('method')
+        item = e.get('params', {}).get('item', {})
+        kind = item.get('type')
+        if method == 'item/started' and kind == 'dynamicToolCall' and item.get('tool') == 'act':
+            act_lines.append(line)
+        if method != 'item/completed' or item.get('id') in seen:
+            continue
+        seen.add(item.get('id'))
+        if kind == 'commandExecution':
+            items.append((line, kind, item.get('command', ''), item.get('durationMs', 0) or 0, item.get('exitCode')))
+        if kind == 'fileChange':
+            text = '\n'.join(c.get('path', '') + '\n' + c.get('diff', '') for c in item.get('changes', []))
+            items.append((line, kind, text, 0, None))
+        if kind == 'dynamicToolCall' and item.get('tool') == 'observe' and 'initial_image_hashes' not in result:
+            content = item.get('contentItems', [])
+            result['initial_image_hashes'] = ','.join(hashlib.sha256(c['imageUrl'].encode()).hexdigest() for c in content if c.get('type') == 'inputImage')
+            texts = [c.get('text','') for c in content if c.get('type') == 'inputText']
+            if texts:
+                try:
+                    result['initial_state'] = json.dumps(json.loads(texts[0]).get('robot_state'), sort_keys=True)
+                except ValueError:
+                    pass
+    cutoff = max(act_lines, default=10**12)
+    items = [x for x in items if x[0] < cutoff]
+    cmds = [x for x in items if x[1] == 'commandExecution']
+    kin = re.compile(r'\bik\b|\bfk\b|jacobian|kinematic|least_squares|inverse.?kin', re.I)
+    def has_kinematics(text):
+        # Embedded image bytes may accidentally contain short identifiers.
+        return bool(kin.search(re.sub(r'[A-Za-z0-9+/=]{200,}', '[binary omitted]', text)))
+    result.update(command_count=len(cmds), command_seconds=sum(x[3] for x in cmds)/1000,
+                  command_failures=sum(x[4] not in (0, None) for x in cmds),
+                  file_changes=sum(x[1] == 'fileChange' for x in items),
+                  kinematics_evidence=any(has_kinematics(x[2]) for x in items),
+                  kinematics_commands=sum(has_kinematics(x[2]) for x in cmds),
+                  first_action_commands=sum(x[0] < min(act_lines, default=10**12) for x in cmds),
+                  event_act_count=len(act_lines))
+    result['kinematics_example'] = next((x[2][:1000] for x in items if has_kinematics(x[2])), '')
+    return result
+
+episodes = []
+for name in NAMES:
+    directory = ROOT / ('embo_' + name)
+    summaries, decisions = {}, collections.defaultdict(list)
+    for p in directory.glob('gpu_*/harness/*.episodes.jsonl'):
+        for h in rows(p):
+            assert h['episode_id'] not in summaries
+            summaries[h['episode_id']] = (h, p)
+    for p in directory.glob('gpu_*/harness/*_eval.jsonl'):
+        for d in rows(p):
+            if d['type'] == 'infer':
+                decisions[d['episode_id']].append(d)
+    for p in directory.glob('gpu_*/robolab/episode_results.jsonl'):
+        for r in rows(p):
+            h, hp = summaries[r['harness_episode_id']]
+            ds = decisions[h['episode_id']]
+            ds.sort(key=lambda d: d['decision_step'])
+            successful_ds = [d for d in ds if d['status']=='ok']
+            assert len(successful_ds) == h['decision_count'], (name, r['task_name'])
+            ep = dict(format=name, task=r['task_name'], run=r['run'], success=int(r['success']),
+                      score=r['score'], attributes=','.join(r['attributes']), reason=r['reason'],
+                      result_path=str(p.relative_to(ROOT)), summary_path=str(hp.relative_to(ROOT)),
+                      episode_id=h['episode_id'], episode_index=h['episode_index'])
+            for key in ['model','reasoning_effort','action_format','max_steps','control_dt','started_at','ended_at',
+                        'status','end_reason','codex_finish','harness_seconds','wall_seconds','raw_harness_seconds',
+                        'raw_wall_seconds','decision_count','executed_control_steps','usage_complete','timing_status',
+                        'rate_limit_count','excluded_seconds','network_wait_seconds']:
+                ep[key] = h.get(key)
+            ep['seconds_per_decision'] = h['harness_seconds']/h['decision_count']
+            ep['inference_requests'] = len(ds)
+            ep['first_decision_raw_seconds'] = ds[0]['elapsed_ms']/1000
+            ep['later_decision_raw_seconds'] = st.mean(d['elapsed_ms']/1000 for d in ds[1:]) if len(ds)>1 else 0
+            ep['infer_status'] = json.dumps(dict(collections.Counter(d['status'] for d in ds)))
+            ep['chunk_sizes'] = ','.join(map(str, sorted({d['target']['steps'] for d in successful_ds})))
+            for key, val in r['metrics'].items():
+                ep['metric_'+key] = val
+            for key, val in r['events'].items():
+                if isinstance(val, (int,float)):
+                    ep['event_'+key] = val
+            for key, val in h['tokens'].items():
+                ep['episode_'+key] = val
+            # Last infer cumulative usage avoids the wrap-up after robot control.
+            for key, val in (successful_ds[-1].get('usage') or {}).get('total', {}).items():
+                ep['control_'+key] = val
+            ep['env_seconds'] = r['timing']['env_step_s']
+            event_path = hp.with_name(hp.name[:-len('.episodes.jsonl')]) / f"episode-{h['episode_index']:04d}" / 'events.jsonl'
+            ep['events_path'] = str(event_path.relative_to(ROOT))
+            ep.update(event_stats(event_path))
+            episodes.append(ep)
+    print('Finished', name, flush=True)
+
+assert len(episodes) == 240
+assert len({(e['format'],e['task'],e['run']) for e in episodes}) == 240
+write_csv('episodes.csv', episodes)
+agg, tasks = [], []
+for name in NAMES:
+    es = [e for e in episodes if e['format']==name]
+    a = dict(format=name, episodes=len(es), successes=sum(e['success'] for e in es),
+             success_rate=st.mean(e['success'] for e in es), mean_score=st.mean(e['score'] for e in es))
+    for key in ['harness_seconds','wall_seconds','decision_count','executed_control_steps','seconds_per_decision',
+                'first_decision_raw_seconds','later_decision_raw_seconds','command_count','command_seconds',
+                'command_failures','first_action_commands','kinematics_commands']:
+        a['mean_'+key] = st.mean(e[key] for e in es)
+        a['median_'+key] = st.median(e[key] for e in es)
+    a['pooled_seconds_per_decision'] = sum(e['harness_seconds'] for e in es)/sum(e['decision_count'] for e in es)
+    for key in ['control_reasoningOutputTokens','control_outputTokens','control_inputTokens','control_cachedInputTokens']:
+        a[key+'_per_decision'] = sum(e.get(key,0) for e in es)/sum(e['decision_count'] for e in es)
+    a['kinematics_episodes'] = sum(e['kinematics_evidence'] for e in es)
+    a['command_episodes'] = sum(e['command_count']>0 for e in es)
+    a['usage_complete_episodes'] = sum(e['usage_complete'] for e in es)
+    a['rate_limit_count'] = sum(e['rate_limit_count'] for e in es)
+    a['excluded_seconds'] = sum(e['excluded_seconds'] for e in es)
+    for key in sorted({k for e in es for k in e if k.startswith('event_') and k!='event_act_count'}):
+        a[key+'_total'] = sum(e.get(key,0) for e in es)
+        a[key+'_per_1000_control_steps'] = 1000*sum(e.get(key,0) for e in es)/sum(e['executed_control_steps'] for e in es)
+    for key in sorted({k for e in es for k in e if k.startswith('metric_')}):
+        a[key+'_median'] = st.median(e[key] for e in es)
+    agg.append(a)
+    for task in sorted({e['task'] for e in es}):
+        ts = [e for e in es if e['task']==task]
+        t = dict(format=name,task=task,successes=sum(e['success'] for e in ts),mean_score=st.mean(e['score'] for e in ts))
+        for key in ['harness_seconds','wall_seconds','decision_count','seconds_per_decision','executed_control_steps']:
+            t['mean_'+key] = st.mean(e[key] for e in ts)
+        tasks.append(t)
+write_csv('summary.csv', agg)
+write_csv('per_task.csv', tasks)
+(OUT/'data.json').write_text(json.dumps(dict(summary=agg,per_task=tasks,episodes=episodes), ensure_ascii=False, indent=2))
+print(json.dumps(agg, indent=2))

harness_robolab_exploration/action_format_analysis_20261029/analyze_per_action.py

@@ -0,0 +1,85 @@
+from pathlib import Path
+import collections,csv,json,re
+import numpy as np
+
+ROOT=Path('/data/NTU_slab/draven/proj/codebase/outputs/robolab_exploration')
+OUT=ROOT/'action_format_analysis_20261029'
+data=json.loads((OUT/'data.json').read_text())
+episodes=[e for e in data['episodes'] if e['format'].startswith('joint_')]
+valid={}
+for fmt in ['joint_abs','joint_delta']:
+    for p in (ROOT/('embo_'+fmt)).glob('gpu_*/harness/*_eval.jsonl'):
+        for raw in p.open():
+            d=json.loads(raw)
+            if d['type']=='infer' and d['status']=='ok':
+                valid[d['action_metadata']['tool_call_id']]=d
+actions=[]
+examples=[]
+types=collections.Counter()
+for e in episodes:
+    pending=[]
+    file_changes=0
+    previous=None
+    act_number=0
+    for line,raw in enumerate((ROOT/e['events_path']).open(),1):
+        complete='"item/completed"' in raw
+        if complete:
+            m=re.search(r'"item":\s*\{\s*"type":\s*"([^"]+)"',raw)
+            if m: types[m[1]]+=1
+        if complete and '"type": "commandExecution"' in raw:
+            item=json.loads(raw)['event']['params']['item']
+            pending.append(dict(line=line,command=item.get('command') or '',output=(item.get('aggregatedOutput') or '')[:6000],exit_code=item.get('exitCode')))
+        if complete and '"type": "fileChange"' in raw:
+            file_changes+=1
+        if '"item/started"' not in raw or '"tool": "act"' not in raw:
+            continue
+        item=json.loads(raw)['event']['params']['item']
+        d=valid.get(item['id'])
+        if d:
+            target=d['target']
+            key='joint_positions' if e['format']=='joint_abs' else 'delta_joint_positions'
+            q=target[key]
+            if key=='delta_joint_positions':
+                arm_hold=max(abs(x) for x in q)<1e-8
+                joint_changes=sum(abs(x)>1e-6 for x in q)
+                max_move=max(abs(x) for x in q)
+            else:
+                diff=np.abs(np.array(q)-np.array(previous[key])) if previous else np.ones(7)*float('nan')
+                arm_hold=bool(np.all(diff<1e-6))
+                joint_changes=int(sum(diff>1e-6))
+                max_move=float(max(diff))
+            row=dict(format=e['format'],task=e['task'],run=e['run'],decision_step=d['decision_step'],
+                     event_line=line,event_path=e['events_path'],command_count=len(pending),
+                     file_changes=file_changes,arm_hold=arm_hold,nonzero_joints=joint_changes,
+                     max_joint_change=max_move,gripper=target['gripper'],
+                     gripper_change=previous is not None and target['gripper']!=previous['gripper'],
+                     target=json.dumps(target),elapsed_seconds=d['elapsed_ms']/1000,
+                     command_lines=','.join(str(x['line']) for x in pending))
+            actions.append(row)
+            if not pending and (act_number<12 or (e['format']=='joint_delta' and 0<joint_changes<=2 and not arm_hold)):
+                examples.append(dict(**row,previous_target=previous))
+            previous=target
+            act_number+=1
+        pending=[]
+        file_changes=0
+
+with (OUT/'per_action_tool_usage.csv').open('w') as f:
+    w=csv.DictWriter(f,fieldnames=list(actions[0]));w.writeheader();w.writerows(actions)
+(OUT/'per_action_direct_examples.json').write_text(json.dumps(examples,indent=2))
+print('Completed item types',dict(types))
+summary=[]
+for fmt in ['joint_abs','joint_delta']:
+    a=[r for r in actions if r['format']==fmt]
+    no=[r for r in a if not r['command_count']]
+    s=dict(format=fmt,valid_actions=len(a),no_shell_since_previous_act=len(no),no_shell_fraction=len(no)/len(a),
+           with_shell=len(a)-len(no),no_shell_and_no_file_change=sum(not r['file_changes'] for r in no),
+           no_shell_arm_hold=sum(r['arm_hold'] for r in no),no_shell_joint_changes_histogram=dict(collections.Counter(r['nonzero_joints'] for r in no)),
+           episodes_with_no_shell_actions=len({(r['task'],r['run']) for r in no}))
+    summary.append(s);print(json.dumps(s))
+print('DELTA DIRECT NONZERO EXAMPLES')
+for e in [e for e in examples if e['format']=='joint_delta' and 0<e['nonzero_joints']<=2][:10]:
+    print(json.dumps(e))
+print('ABS DIRECT CHANGED EXAMPLES')
+for e in [e for e in examples if e['format']=='joint_abs' and e['max_joint_change']>.15][:8]:
+    print(json.dumps(e))
+(OUT/'per_action_tool_summary.json').write_text(json.dumps(summary,indent=2))

harness_robolab_exploration/action_format_analysis_20261029/bootstrap.json

@@ -0,0 +1,27 @@
+{
+  "method": "paired task cluster percentile bootstrap",
+  "replicates": 20000,
+  "seed": 20261029,
+  "intervals_95": {
+    "joint_delta_minus_eef_abs_success_pp": [
+      -11.666666666666666,
+      20.0
+    ],
+    "joint_minus_eef_success_pp": [
+      -9.166666666666666,
+      12.5
+    ],
+    "joint_vs_eef_seconds_per_decision_ratio": [
+      1.296127775439581,
+      1.602046285161154
+    ],
+    "eef_delta_vs_absolute_hit_table_rate_ratio": [
+      0.4203980517078633,
+      0.8197583181795426
+    ],
+    "joint_delta_vs_absolute_hit_table_rate_ratio": [
+      0.43397238058952137,
+      1.0402528130881765
+    ]
+  }
+}

harness_robolab_exploration/action_format_analysis_20261029/make_report.py

@@ -0,0 +1,146 @@
+from pathlib import Path
+import json
+import statistics as st
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+OUT=Path('/data/NTU_slab/draven/proj/codebase/outputs/robolab_exploration/action_format_analysis_20261029')
+data=json.loads((OUT/'data.json').read_text())
+es=data['episodes']
+names=['eef_abs','eef_delta','joint_abs','joint_delta']
+labels=['EEF absolute','EEF delta','Joint absolute','Joint delta']
+short=['EEF abs','EEF delta','Joint abs','Joint delta']
+colors=['#3688b7','#73b9d4','#d67b33','#e7ad59']
+summary={a['format']:a for a in data['summary']}
+tasks=sorted({e['task'] for e in es})
+keys=['success','harness_seconds','decision_count','event_GRIPPER_HIT_TABLE','executed_control_steps']
+arr=np.array([[[sum(e.get(k,0) for e in es if e['format']==n and e['task']==t) for k in keys] for n in names] for t in tasks])
+rng=np.random.default_rng(20261029)
+sampled=arr[rng.integers(0,len(tasks),(20000,len(tasks)))].sum(axis=1)
+boot={
+    'joint_delta_minus_eef_abs_success_pp':(sampled[:,3,0]-sampled[:,0,0])/60*100,
+    'joint_minus_eef_success_pp':(sampled[:,2:,0].sum(axis=1)-sampled[:,:2,0].sum(axis=1))/120*100,
+    'joint_vs_eef_seconds_per_decision_ratio':(sampled[:,2:,1].sum(axis=1)/sampled[:,2:,2].sum(axis=1))/(sampled[:,:2,1].sum(axis=1)/sampled[:,:2,2].sum(axis=1)),
+    'eef_delta_vs_absolute_hit_table_rate_ratio':(sampled[:,1,3]/sampled[:,1,4])/(sampled[:,0,3]/sampled[:,0,4]),
+    'joint_delta_vs_absolute_hit_table_rate_ratio':(sampled[:,3,3]/sampled[:,3,4])/(sampled[:,2,3]/sampled[:,2,4]),
+}
+intervals={k:np.quantile(v,[.025,.975]).tolist() for k,v in boot.items()}
+(OUT/'bootstrap.json').write_text(json.dumps(dict(method='paired task cluster percentile bootstrap',replicates=20000,seed=20261029,intervals_95=intervals),indent=2))
+
+plt.rcParams.update({'font.size':10,'axes.spines.top':False,'axes.spines.right':False,'pdf.fonttype':42})
+fig=plt.figure(figsize=(16,10),layout='constrained')
+gs=fig.add_gridspec(2,3,width_ratios=[1,1,1.55])
+panels=[(100*np.array([summary[n]['success_rate'] for n in names]),'Task success','Success (%)',60),
+        ([summary[n]['pooled_seconds_per_decision'] for n in names],'Decision latency','Seconds / decision',20),
+        ([summary[n]['mean_command_count'] for n in names],'Shell commands during control','Commands / episode',53),
+        ([summary[n]['event_GRIPPER_HIT_TABLE_per_1000_control_steps'] for n in names],'Gripper-table contact events','Events / 1,000 control frames',3.3)]
+for ix,(vals,title,ylabel,limit) in enumerate(panels):
+    ax=fig.add_subplot(gs[ix//2,ix%2])
+    ax.bar(short,vals,color=colors,width=.65)
+    for j,v in enumerate(vals):
+        label=f'{v:.1f}'
+        if ix==0: label+=f"%\n({summary[names[j]]['successes']}/60)"
+        ax.text(j,v+limit*.025,label,ha='center',va='bottom',fontsize=10)
+    ax.set(title=title,ylabel=ylabel,ylim=(0,limit))
+    ax.tick_params(axis='x',labelrotation=22)
+    ax.grid(axis='y',alpha=.17)
+    ax.set_axisbelow(True)
+ax=fig.add_subplot(gs[:,2])
+counts=np.array([[next(r['successes'] for r in data['per_task'] if r['task']==t and r['format']==n) for n in names] for t in tasks])
+ax.imshow(counts,vmin=0,vmax=3,cmap='Blues',aspect='auto')
+ax.set_xticks(range(4),short,rotation=25,ha='right')
+ax.set_yticks(range(len(tasks)),[t.removesuffix('Task') for t in tasks],fontsize=9)
+for i in range(len(tasks)):
+    for j in range(4): ax.text(j,i,f'{counts[i,j]}/3',ha='center',va='center',color='white' if counts[i,j]>=2 else '#12344c',fontsize=10)
+ax.set_title('Success by task (3 episodes per cell)',pad=14)
+fig.suptitle('RoboLab action formats: comparable success, different computation costs\ngpt-6-astra / medium | 20 tasks x 3 episodes per format | 15-frame actions',fontsize=15)
+fig.savefig(OUT/'comparison.png',dpi=180)
+fig.savefig(OUT/'comparison.pdf')
+plt.close(fig)
+
+lines=['# RoboLab 四种动作格式实验分析','',
+'分析日期：2026-10-29。数据：`embo_eef_abs`、`embo_eef_delta`、`embo_joint_abs`、`embo_joint_delta`。未纳入 chunk8/chunk30/adaptive 实验。','',
+'这组结果支持：GPT agent 能适应四种动作接口，在本任务集上保持接近的任务成功率；joint 接口需要更高的在线决策成本。日志显示，joint 的适应高度依赖模型自行构建和使用运动学程序，因此“joint 更难理解”只是可能解释，不能直接从耗时推出。','',
+'## 数据与口径','',
+'- 四组均为 `gpt-6-astra`、`medium`，20 个相同任务，每个任务 3 次，共 240 条结果；固定 action chunk=15，控制周期 1/15 秒；同步推理，无 plan、VLA 和共享记忆。',
+'- 以 `harness_episode_id` 连接 benchmark 的 `episode_results.jsonl` 和 harness 的 `*.episodes.jsonl`；成功由 benchmark 的 `success` 定义。',
+'- “每次决策耗时”=控制阶段 `harness_seconds` 总和 / `decision_count` 总和。它已扣除日志识别到的网络/限流等待，不含环境执行和结束后的总结，但仍包含模型响应、工具往返及程序执行��不能叫纯模型思考时间。',
+'- 240 条轨迹的主控制阶段 timing 均完整，限流事件合计为 0。首轮/后续耗时来自 infer 的原始 elapsed_ms，未按阶段扣除网络等待。',
+'- joint_delta 有 1 条策略执行错误（`RubiksCubeThenBananaTask` 第 3 次，夹爪值不合法），保留为失败；其 32 个有效动作后还有 1 次失败请求。其它 239 条正常到达 benchmark 终止条件。',
+'- 18 条轨迹的结束总结超时，加上上述执行错误，合计 19 条 `usage_complete=false`。因此 token 比较使用最后一个有效 infer 的累计 usage，避开事后总结，并将其视为诊断性统计。',
+'- 各格式使用不同 GPU，并且轨迹长度不同导致后续请求时间不同；日志中的网络等待扣除并不能消除所有服务端延迟或硬件影响。相同 task/run 的初始机器人状态一致，但没有假定图像或随机布局逐像素配对。','',
+'## 汇总','',
+'| 格式 | 成功率 | 平均 score | 决策耗时 s/次 | 平均决策数 | 平均控制推理 s/轨迹 | 平均 wall s/轨迹 |',
+'|---|---:|---:|---:|---:|---:|---:|']
+for n,l in zip(names,labels):
+    a=summary[n]
+    lines.append(f"| {l} | {a['successes']}/60 ({a['success_rate']*100:.1f}%) | {a['mean_score']:.3f} | {a['pooled_seconds_per_decision']:.2f} | {a['mean_decision_count']:.1f} | {a['mean_harness_seconds']:.1f} | {a['mean_wall_seconds']:.1f} |")
+lines += ['', '## 1. 更明确的发现是：模型自行构建接口转换程序','',
+'两种 joint 格式的 120/120 条轨迹，都在控制阶段的命令或文件修改中出现了运动学计算证据。检测只读取公开工具调用，不读取 reasoning 文本；匹配 IK/FK、Jacobian、kinematics、least_squares 等标识，并先剔除长 base64 图像字符串。下面的实例直接包含 FK 和数值 IK 代码，因此可以确认实际发生了坐标/关节转换。','',
+'| 格式 | 有运动学代码证据的轨迹 | 平均 shell 命令数 | 程序执行时间 s/轨迹 | 控制阶段 reasoning tokens/动作 | 控制阶段 output tokens/动作 |',
+'|---|---:|---:|---:|---:|---:|']
+for n,l in zip(names,labels):
+    a=summary[n]
+    lines.append(f"| {l} | {a['kinematics_episodes']}/60 | {a['mean_command_count']:.1f} | {a['mean_command_seconds']:.1f} | {a['control_reasoningOutputTokens_per_decision']:.1f} | {a['control_outputTokens_per_decision']:.1f} |")
+lines += ['',
+'例如 joint_absolute 的 PlasticBottles 第 1 次轨迹，模型创建 `ik.py`，实现 FK，再用 `scipy.optimize.least_squares` 优化位置与姿态误差，随后执行 `python ik.py \'[0.45,0,0.47]\'`，把求出的关节角传给 `act`。joint_delta 同样构建 FK/IK 程序，再输出相对关节变化。',
+'',
+'这更支持“agent 能用程序构建动作接口适配层”。这些案例仍以空间位置/姿态为计算目标；不能因此断言模型直接、原生地在关节空间规划，也不能据此推广到未测过的机器人形态或任意动作接口。',
+'',
+'程序真正运行的时间平均只有约 15–16 秒/条 joint 轨迹，远小于两类接口之间的数百秒控制耗时差。额外程序构建、工具往返和模型生成是合理解释；现有日志不足以精确分解它们各自的因果贡献。','',
+'## 2. joint 的代价持续存在，主要分界是 EEF/joint','',
+'| 格式 | 首次动作平均耗时 s | 后续动作平均耗时 s |',
+'|---|---:|---:|']
+for n,l in zip(names,labels):
+    a=summary[n]
+    lines.append(f"| {l} | {a['mean_first_decision_raw_seconds']:.1f} | {a['mean_later_decision_raw_seconds']:.1f} |")
+lines += ['',
+'后续动作先在每条轨迹内平均，再对 60 条轨迹平均。joint 不仅初始化更慢，后续决策仍更慢。按任务平均的每动作时间比较，20/20 个任务上 joint 两组合并都慢于 EEF 两组合并。',
+'',
+'同一空间内，absolute/delta 的合并每动作耗时只差约 2%–3%；joint 相对 EEF 则增加约 44%–45%。合并 joint/EEF 耗时比的任务分组 bootstrap 95% 区间为 '+f"[{intervals['joint_vs_eef_seconds_per_decision_ratio'][0]:.2f}, {intervals['joint_vs_eef_seconds_per_decision_ratio'][1]:.2f}]。",'',
+'本实验限制的是模拟控制步数，没有给四组相同的真实时间或 token 预算。因此成功率接近，表示在允许更多在线计算时，joint 可以维持接近的任务表现；不能直接推出实时控制能力相同。','',
+'## 3. 总分相近，任务偏好明显不同','',
+'| 任务 | EEF abs | EEF delta | Joint abs | Joint delta |',
+'|---|---:|---:|---:|---:|']
+for task in ['SpoonInMugTask','BlockStackingSpecifiedOrderTask','BananaThenRubiksCubeTask','TakeSpatulaOffShelfTask','FruitsOnionToPlateTask']:
+    row=counts[tasks.index(task)]
+    lines.append('| '+task+' | '+' | '.join(f'{x}/3' for x in row)+' |')
+lines += ['',
+'勺子入杯是较明确的互补例子：joint 合计 4/6，EEF 0/6。取铲子则 EEF 3/6，joint 1/6。可以提出“接口/控制器改变可行姿态、接近路径及局部操作方式”的假设，但每格仅 3 次，不能据此确定某个格式稳定擅长某类操作。',
+'',
+'尤其需要注意：EEF 在环境端使用 IK 控制器、空间位置插值和 SLERP；joint 使用关节控制器和关节线性插值。因此实验同时改变了动作表示、轨迹插值与底层控制路径，不是只换 JSON 字段。','',
+'## 4. delta 的共同信号是更少的撞桌事件','',
+'| 格式 | GRIPPER_HIT_TABLE 总数 | 每 1000 控制步次数 | 出现过撞桌的轨迹 |',
+'|---|---:|---:|---:|']
+for n,l in zip(names,labels):
+    a=summary[n]
+    hits=sum(e.get('event_GRIPPER_HIT_TABLE',0)>0 for e in es if e['format']==n)
+    lines.append(f"| {l} | {a['event_GRIPPER_HIT_TABLE_total']} | {a['event_GRIPPER_HIT_TABLE_per_1000_control_steps']:.2f} | {hits}/60 |")
+lines += ['',
+'相对 absolute，EEF delta 的按步归一化撞桌率下降约 39%，joint delta 下降约 33%。这与相对动作更便于局部修正的解释一致，但尚未证明该机制。任务分组 bootstrap 的 delta/absolute 撞桌率比区间：EEF '+f"[{intervals['eef_delta_vs_absolute_hit_table_rate_ratio'][0]:.2f}, {intervals['eef_delta_vs_absolute_hit_table_rate_ratio'][1]:.2f}]，joint [{intervals['joint_delta_vs_absolute_hit_table_rate_ratio'][0]:.2f}, {intervals['joint_delta_vs_absolute_hit_table_rate_ratio'][1]:.2f}]。"+'后者跨过 1，证据相对更弱。',
+'',
+'这些是 benchmark 记录的事件次数，不是独立事故数；未将 TARGET_OBJECT_DROPPED 当成失败，因为放置时的主动释放也可能产生相关事件。','',
+'## 5. 部分瓶颈跨格式保持不变','',
+'洋葱放盘子 0/12，mustard 放左箱 1/12，白杯重定向 2/12。对于洋葱任务，各格式的结果都未满足目标物体抓取条件；只换动作接口未解决该问题。需要结合视频/逐步观察区分视觉定位、抓取几何、接触控制和评分判定，终止原因本身不足以确定根因。','',
+'## 统计边界和下一步','',
+'joint_delta 的成功率最高，但仅比 EEF absolute 多成功 3 条。以任务为分组单位，同时重采样四个格式的 20 个任务，进行 20,000 次 percentile bootstrap：差值 5 个百分点的 95% 区间为 '+f"[{intervals['joint_delta_minus_eef_abs_success_pp'][0]:.1f}, {intervals['joint_delta_minus_eef_abs_success_pp'][1]:.1f}] 个百分点。"+'这不支持确定的格式胜负，也没有建立严格的统计等效性。区间只反映这组任务和重复的采样变异；不涵盖模型更新、提示词变化或其他机器人。',
+'',
+'最有信息量的后续实验：',
+'',
+'1. 为 joint 预置统一、经过校验的 IK helper，与当前自行构建 IK 的条件比较；分开量化工具构建与反复调用的成本，并尽量对齐 EEF 的控制器/插值。',
+'2. 统一真实时间或控制阶段 token 预算，再比较成功率，检验更多计算是否在补偿接口差异。',
+'3. 对勺子入杯、取铲子、堆叠等有交叉结果的任务增加重复；对洋葱任务做视频和逐步失败分析。','',
+'## 完整任务结果','',
+'| 任务 | EEF abs | EEF delta | Joint abs | Joint delta |',
+'|---|---:|---:|---:|---:|']
+for task,row in zip(tasks,counts): lines.append('| '+task+' | '+' | '.join(f'{x}/3' for x in row)+' |')
+lines += ['', '## 文件与复现','',
+'- [比较图](comparison.png) / [PDF](comparison.pdf)',
+'- [汇总](summary.csv)、[逐任务结果](per_task.csv)、[逐轨迹数据](episodes.csv)、[bootstrap](bootstrap.json)',
+'- `analyze.py` 从原始日志抽取数据，`make_report.py` 生成报告与图表；无需网络或模型调用。',
+'- 逐轨迹 CSV 的 result_path / summary_path / events_path 均相对上一级 `robolab_exploration` 目录，可以回查来源。',
+'- 工具统计只包含最后一次动作开始以前的已完成 shell 命令/文件修改，排除任务结束后总结。运动学标识匹配是可审计的日志代理指标，不把任意 shell 调用都算成 IK。','']
+(OUT/'report.md').write_text('\n'.join(lines))
+print('Report and plots written:',OUT)

harness_robolab_exploration/action_format_analysis_20261029/per_action_report.md

@@ -0,0 +1,30 @@
+# Joint 动作是否每次都求解 IK
+
+核对范围：joint_absolute 的 3162 个有效动作，以及 joint_delta 的 2875 个有效动作，共 6037 个。joint_delta 的 1 次非法夹爪参数请求未计入有效动作。
+
+“每条轨迹都出现过运动学计算”不代表“每次动作都求解 IK”。按相邻两次 act 调用之间的公开工具调用检查：
+
+| 格式 | 有效动作 | 动作前无新的 shell 命令 | 比例 |
+|---|---:|---:|---:|
+| Joint absolute | 3162 | 1110 | 35.1% |
+| Joint delta | 2875 | 658 | 22.9% |
+
+这些无 shell 的间隔也都没有文件修改。此项统计可以支持“没有为每次动作重新调用外部求解程序”，但不能区分心算、复制之前计算结果、或复用预先批量计算的目标，不能直接当成“原生关节规划比例”。反过来，有 shell 调用也不一定是求解 IK，还可能是 FK、差值计算、图片处理等。
+
+在 1110 个无新 shell 的 absolute 动作中，952 个重复了上一个关节目标（容差 1e-6 rad）。重复目标不等于机械臂实际完全不动，也可能继续向目标靠近。在 658 个无新 shell 的 delta 动作中，599 个为零关节增量。这两类动作合计占无新 shell 动作的 87.7%，常用于保持机械臂目标、开合夹爪或等待。
+
+另外，joint_delta 有 59 个非零增量动作前也没有新的 shell 调用。因此确实存在直接提交非零关节调整的情况。
+
+例如 ClampInRightBinTask 第 2 次轨迹，decision_step=18（第 19 个动作），模型直接提交：
+
+```json
+{"delta_joint_positions": [0, -0.07, 0, 0, 0, -0.07, 0], "gripper": 1}
+```
+
+即直接调整第 2、6 个关节，前一个动作是全零增量，两者之间没有新的命令执行或文件修改。同一条轨迹还出现了 -0.14、-0.10 等类似直接调整。
+
+[该动作原始日志](../embo_joint_delta/gpu_4/harness/20261028T143501.680089Z_eval/episode-0011/events.jsonl:508)
+
+[逐动作统计](per_action_tool_usage.csv) · [汇总 JSON](per_action_tool_summary.json) · [直接输出实例](per_action_direct_examples.json)
+
+方法：通过 infer 的 action_metadata.tool_call_id 对齐 events.jsonl 的 act 开始事件，只统计 status=ok 的动作。每个 act 开始时统计自上次 act 开始以来已完成的 commandExecution 和 fileChange；首个动作从轨迹开始统计。工具完成事件还包括 imageView，但未发现其他外部计算工具类型。未读取 reasoning 文本。可用 analyze_per_action.py 复现。

harness_robolab_exploration/action_format_analysis_20261029/per_action_tool_summary.json

@@ -0,0 +1,38 @@
+[
+  {
+    "format": "joint_abs",
+    "valid_actions": 3162,
+    "no_shell_since_previous_act": 1110,
+    "no_shell_fraction": 0.3510436432637571,
+    "with_shell": 2052,
+    "no_shell_and_no_file_change": 1110,
+    "no_shell_arm_hold": 952,
+    "no_shell_joint_changes_histogram": {
+      "7": 138,
+      "0": 952,
+      "2": 2,
+      "4": 1,
+      "3": 4,
+      "5": 1,
+      "1": 12
+    },
+    "episodes_with_no_shell_actions": 59
+  },
+  {
+    "format": "joint_delta",
+    "valid_actions": 2875,
+    "no_shell_since_previous_act": 658,
+    "no_shell_fraction": 0.2288695652173913,
+    "with_shell": 2217,
+    "no_shell_and_no_file_change": 658,
+    "no_shell_arm_hold": 599,
+    "no_shell_joint_changes_histogram": {
+      "0": 599,
+      "7": 37,
+      "3": 2,
+      "2": 10,
+      "1": 10
+    },
+    "episodes_with_no_shell_actions": 60
+  }
+]

harness_robolab_exploration/action_format_analysis_20261029/per_task.csv

@@ -0,0 +1,81 @@
+format,task,successes,mean_score,mean_harness_seconds,mean_wall_seconds,mean_decision_count,mean_seconds_per_decision,mean_executed_control_steps
+eef_abs,BananaInBowlTask,3,1.0,157.33171032369137,211.8709411000212,17,9.247443185279183,244
+eef_abs,BananaThenRubiksCubeTask,2,0.8333333333333334,496.64874226351577,697.6807341476282,59.666666666666664,8.324276635466099,891
+eef_abs,BananasInBinThreeTotalTask,2,0.6666666666666666,285.82865926623344,428.3455421378215,33.333333333333336,8.237143252167186,490.6666666666667
+eef_abs,BlockStackingSpecifiedOrderTask,1,0.6633333464463552,1148.0135238071282,1447.5446647653978,83,13.737848865990669,1244.6666666666667
+eef_abs,BowlStackingRightOnLeftTask,2,0.6666666666666666,152.1140338430802,200.6501354649663,15,10.273769573918926,218.66666666666666
+eef_abs,ClampInRightBinTask,1,0.3333333333333333,430.69484340399504,611.5088439484438,43.666666666666664,10.091985567994039,650.3333333333334
+eef_abs,FoodPacking1BoxesTask,2,0.6666666666666666,282.455264441669,390.5908410300811,29.666666666666668,9.024675322152556,436.6666666666667
+eef_abs,FoodPacking1CansTask,2,0.6666666666666666,257.58975758651894,376.2833057194948,32,7.651042560956846,475.3333333333333
+eef_abs,FruitsOnionToPlateTask,0,0.0,593.3265360544125,1205.913701608777,60,9.888775600906875,900
+eef_abs,MarkerInMugTask,0,0.0,460.4224292685588,815.2626259078583,40,11.51056073171397,600
+eef_abs,MustardInLeftBinTask,0,0.0,238.25947384784618,337.27589159458876,30,7.941982461594873,450
+eef_abs,OneBottleInSquarePailTask,0,0.0,578.8833134397864,871.6764087875684,60,9.64805522399644,900
+eef_abs,PlasticBottlesInSquarePailTask,1,0.5555555621782938,1982.2050078461568,2742.6630756631494,154,12.417650535167043,2305.3333333333335
+eef_abs,RedItemsInBinTask,3,1.0,227.70295774191618,340.60613404711086,30,7.5297355755878534,436.3333333333333
+eef_abs,ReorientRedMugTask,2,0.8333333333333334,423.23770123968524,594.8859411999583,36.333333333333336,11.15487383712306,542.6666666666666
+eef_abs,ReorientWhiteMugsTask,0,0.3333333333333333,613.0482010915875,887.0017536530892,60,10.217470018193126,900
+eef_abs,RubiksCubeThenBananaTask,2,0.8333333333333334,362.60824218640727,497.12937189638615,39,8.662882608279975,579
+eef_abs,SmallPumpkinInBinTask,1,0.3333333333333333,434.69160879155,931.2127482940754,45.666666666666664,9.168814107213242,680.6666666666666
+eef_abs,SpoonInMugTask,0,0.0,651.6000350589553,1129.6348898460467,60,10.860000584315923,900
+eef_abs,TakeSpatulaOffShelfTask,2,1.0,560.7927742948135,774.3988811994592,41.666666666666664,13.600581977989329,618
+eef_delta,BananaInBowlTask,3,1.0,170.61003639300665,245.58598787585893,23,7.54137210593641,335.6666666666667
+eef_delta,BananaThenRubiksCubeTask,2,0.8333333333333334,437.83089039474726,609.9108054613074,51.666666666666664,8.43149204712414,769
+eef_delta,BananasInBinThreeTotalTask,1,0.3333333333333333,417.8495548640688,636.2352744663755,48.333333333333336,8.602179549700683,720.3333333333334
+eef_delta,BlockStackingSpecifiedOrderTask,2,0.8866666754086813,801.7456773047646,1046.8469891771674,69,11.751491386174301,1032.3333333333333
+eef_delta,BowlStackingRightOnLeftTask,2,0.6666666666666666,139.00534501175085,190.5707634265224,16.333333333333332,8.429878240650872,241.33333333333334
+eef_delta,ClampInRightBinTask,1,0.3333333333333333,683.4482992142439,917.8429108560085,58,11.79249885868235,865.3333333333334
+eef_delta,FoodPacking1BoxesTask,1,0.3333333333333333,431.1946396629016,593.0484345530471,44,10.2619470273455,657.3333333333334
+eef_delta,FoodPacking1CansTask,2,0.6666666666666666,292.6112344885866,422.41389064490795,34.666666666666664,8.069417677928854,513.6666666666666
+eef_delta,FruitsOnionToPlateTask,0,0.0,581.3195285697778,1191.4062597180407,60,9.688658809496296,900
+eef_delta,MarkerInMugTask,1,0.3333333333333333,427.5246796111266,762.7286836082736,39,10.929589826338463,580.3333333333334
+eef_delta,MustardInLeftBinTask,0,0.0,240.00468187779188,338.31963086873293,30,8.000156062593064,450
+eef_delta,OneBottleInSquarePailTask,2,0.6666666666666666,407.739516834418,615.4544245004654,43.666666666666664,8.891416603596287,645.6666666666666
+eef_delta,PlasticBottlesInSquarePailTask,1,0.5555555621782938,1876.3773759901524,2636.415302877625,156.33333333333334,12.157617755224242,2340.3333333333335
+eef_delta,RedItemsInBinTask,3,1.0,275.02442498505116,414.743642916282,37,7.323449287492912,541.6666666666666
+eef_delta,ReorientRedMugTask,1,0.5,459.66966091593105,665.733958038191,43.666666666666664,10.174645436833604,650.6666666666666
+eef_delta,ReorientWhiteMugsTask,1,0.6666666666666666,561.9954654201865,803.9856229598323,54,10.383320653349871,806.6666666666666
+eef_delta,RubiksCubeThenBananaTask,2,0.8333333333333334,384.81587707499665,550.3782687038183,47,8.17569594953623,703.6666666666666
+eef_delta,SmallPumpkinInBinTask,1,0.3333333333333333,552.432050563395,1146.5287855317194,54.333333333333336,10.000190483605445,810.3333333333334
+eef_delta,SpoonInMugTask,0,0.0,823.4626084168752,1295.6939352924626,60,13.72437680694792,900
+eef_delta,TakeSpatulaOffShelfTask,1,0.3333333333333333,814.7249441420039,1130.0309328089158,60,13.578749069033398,895.6666666666666
+joint_abs,BananaInBowlTask,2,0.6666666666666666,548.9788940772414,662.5443975503246,37,14.80496810995288,547
+joint_abs,BananaThenRubiksCubeTask,2,0.8333333333333334,664.0058590422074,814.6082179397345,46.666666666666664,14.164015553482912,691.6666666666666
+joint_abs,BananasInBinThreeTotalTask,2,0.6666666666666666,598.4158837000529,780.8404872318109,43.666666666666664,14.153751256178671,653.6666666666666
+joint_abs,BlockStackingSpecifiedOrderTask,2,0.8866666754086813,1336.0293848887086,1585.3320201237996,72.33333333333333,17.539392086789082,1080.3333333333333
+joint_abs,BowlStackingRightOnLeftTask,2,0.6666666666666666,289.70617979516584,345.6004121278723,18.333333333333332,15.821590443776032,270
+joint_abs,ClampInRightBinTask,1,0.3333333333333333,859.8499358470241,1088.3176321561139,56.666666666666664,15.023782777521346,845.3333333333334
+joint_abs,FoodPacking1BoxesTask,1,0.3333333333333333,807.3321814288696,1014.0410252014796,58.333333333333336,13.878203255054775,874
+joint_abs,FoodPacking1CansTask,1,0.3333333333333333,674.7286364212632,864.8404102201263,51.666666666666664,12.617419836522332,774.6666666666666
+joint_abs,FruitsOnionToPlateTask,0,0.0,793.216106797258,1375.2911678676803,60,13.220268446620969,900
+joint_abs,MarkerInMugTask,1,0.3333333333333333,801.1292025347551,1113.597647326688,37.333333333333336,21.56599893757763,556
+joint_abs,MustardInLeftBinTask,0,0.0,403.9147679383556,500.05526126921177,30,13.463825597945187,450
+joint_abs,OneBottleInSquarePailTask,1,0.3333333333333333,900.2281262055039,1185.3883688996236,58.666666666666664,15.313378301159375,875.3333333333334
+joint_abs,PlasticBottlesInSquarePailTask,1,0.5555555621782938,1756.3035425543785,2422.143421692153,136.33333333333334,13.667128870306495,2040.3333333333333
+joint_abs,RedItemsInBinTask,2,0.8333333333333334,564.8174700712165,771.8016387720903,56.666666666666664,9.948400145487653,844
+joint_abs,ReorientRedMugTask,1,0.3333333333333333,667.1982083022594,870.5517074887952,44.333333333333336,15.324623886228371,661.3333333333334
+joint_abs,ReorientWhiteMugsTask,0,0.6666666666666666,1142.4760430082679,1406.6206423565745,60,19.04126738347113,900
+joint_abs,RubiksCubeThenBananaTask,3,1.0,439.83250731478137,538.8392412041625,30,14.657907924058193,437.6666666666667
+joint_abs,SmallPumpkinInBinTask,2,0.6666666666666666,711.7294865697622,1128.7065327142675,41,17.374131310761012,605.6666666666666
+joint_abs,SpoonInMugTask,2,0.6666666666666666,1222.5779660915334,1636.9450838143628,55,22.14103288152718,816
+joint_abs,TakeSpatulaOffShelfTask,0,0.3333333333333333,1099.6991764654715,1399.2563767482836,60,18.32831960775786,900
+joint_delta,BananaInBowlTask,3,1.0,256.58037315060693,310.3428628742695,18.666666666666668,13.798290659513588,269.3333333333333
+joint_delta,BananaThenRubiksCubeTask,0,0.3333333333333333,794.2506490076581,996.2066375613213,60,13.237510816794302,900
+joint_delta,BananasInBinThreeTotalTask,2,0.6666666666666666,388.61772524068755,526.4775738567114,34.333333333333336,11.191079382240105,507.6666666666667
+joint_delta,BlockStackingSpecifiedOrderTask,3,1.0,650.2710563391447,805.3050919920206,44.666666666666664,15.03821140987353,665
+joint_delta,BowlStackingRightOnLeftTask,2,0.6666666666666666,281.87035231043893,329.09998815009993,16,17.91451734278558,235.33333333333334
+joint_delta,ClampInRightBinTask,0,0.0,1074.4695273935795,1317.635833715399,60,17.907825456559657,900
+joint_delta,FoodPacking1BoxesTask,0,0.0,728.4763367623091,931.7668327689171,60,12.141272279371817,900
+joint_delta,FoodPacking1CansTask,2,0.6666666666666666,465.7221688355009,597.7715336059531,38.666666666666664,13.85529495447667,573.6666666666666
+joint_delta,FruitsOnionToPlateTask,0,0.0,809.1822573120395,1389.716039992869,60,13.486370955200659,900
+joint_delta,MarkerInMugTask,1,0.3333333333333333,779.8481781681379,1085.199661128223,37,21.171852841953275,550.3333333333334
+joint_delta,MustardInLeftBinTask,1,0.3333333333333333,284.0819425135851,361.3227309435606,25,11.32225859918528,370.3333333333333
+joint_delta,OneBottleInSquarePailTask,1,0.3333333333333333,761.6669654076298,995.9184413353602,49,15.171014792568705,730.3333333333334
+joint_delta,PlasticBottlesInSquarePailTask,0,0.4444444576899211,2367.8147331650057,3251.2220837548375,180,13.154526295361144,2700
+joint_delta,RedItemsInBinTask,3,1.0,397.3958667938908,531.9104844306906,36.666666666666664,10.893747593717476,536
+joint_delta,ReorientRedMugTask,3,1.0,393.503084205091,500.02037122348946,24,16.10707621480817,352.6666666666667
+joint_delta,ReorientWhiteMugsTask,1,0.3333333333333333,825.9302600249648,1029.8379659255345,45.666666666666664,19.59923691087299,685
+joint_delta,RubiksCubeThenBananaTask,2,0.6666666666666666,523.2874497820934,659.6150109966596,42.666666666666664,12.301488670589979,631
+joint_delta,SmallPumpkinInBinTask,2,0.6666666666666666,629.6838667045037,1014.2416516169906,37,16.44275810651512,545.6666666666666
+joint_delta,SpoonInMugTask,2,0.6666666666666666,840.9090536137422,1113.288448639214,36.666666666666664,19.05717047141242,540.6666666666666
+joint_delta,TakeSpatulaOffShelfTask,1,0.6666666666666666,1138.0324030766885,1392.2915288234751,52.333333333333336,20.95388038470088,782.3333333333334

harness_robolab_exploration/action_format_analysis_20261029/report.md

@@ -0,0 +1,130 @@
+# RoboLab 四种动作格式实验分析
+
+分析日期：2026-10-29。数据：`embo_eef_abs`、`embo_eef_delta`、`embo_joint_abs`、`embo_joint_delta`。未纳入 chunk8/chunk30/adaptive 实验。
+
+这组结果支持：GPT agent 能适应四种动作接口，在本任务集上保持接近的任务成功率；joint 接口需要更高的在线决策成本。日志显示，joint 的适应高度依赖模型自行构建和使用运动学程序，因此“joint 更难理解”只是可能解释，不能直接从耗时推出。
+
+## 数据与口径
+
+- 四组均为 `gpt-6-astra`、`medium`，20 个相同任务，每个任务 3 次，共 240 条结果；固定 action chunk=15，控制周期 1/15 秒；同步推理，无 plan、VLA 和共享记忆。
+- 以 `harness_episode_id` 连接 benchmark 的 `episode_results.jsonl` 和 harness 的 `*.episodes.jsonl`；成功由 benchmark 的 `success` 定义。
+- “每次决策耗时”=控制阶段 `harness_seconds` 总和 / `decision_count` 总和。它已扣除日志识别到的网络/限流等待，不含环境执行和结束后的总结，但仍包含模型响应、工具往返及程序执行，不能叫纯模型思考时间。
+- 240 条轨迹的主控制阶段 timing 均完整，限流事件合计为 0。首轮/后续耗时来自 infer 的原始 elapsed_ms，未按阶段扣除网络等待。
+- joint_delta 有 1 条策略执行错误（`RubiksCubeThenBananaTask` 第 3 次，夹爪值不合法），保留为失败；其 32 个有效动作后还有 1 次失败请求。其它 239 条正常到达 benchmark 终止条件。
+- 18 条轨迹的结束总结超时，加上上述执行错误，合计 19 条 `usage_complete=false`。因此 token 比较使用最后一个有效 infer 的累计 usage，避开事后总结，并将其视为诊断性统计。
+- 各格式使用不同 GPU，并且轨迹长度不同导致后续请求时间不同；日志中的网络等待扣除并不能消除所有服务端延迟或硬件影响。相同 task/run 的初始机器人状态一致，但没有假定图像或随机布局逐像素配对。
+
+## 汇总
+
+| 格式 | 成功率 | 平均 score | 决策耗时 s/次 | 平均决策数 | 平均控制推理 s/轨迹 | 平均 wall s/轨迹 |
+|---|---:|---:|---:|---:|---:|---:|
+| EEF absolute | 26/60 (43.3%) | 0.519 | 10.66 | 48.5 | 516.9 | 774.6 |
+| EEF delta | 27/60 (45.0%) | 0.514 | 10.47 | 51.5 | 539.0 | 810.7 |
+| Joint absolute | 26/60 (43.3%) | 0.522 | 15.45 | 52.7 | 814.1 | 1075.3 |
+| Joint delta | 29/60 (48.3%) | 0.539 | 15.02 | 47.9 | 719.6 | 957.0 |
+
+## 1. 更明确的发现是：模型自行构建接口转换程序
+
+两种 joint 格式的 120/120 条轨迹，都在控制阶段的命令或文件修改中出现了运动学计算证据。检测只读取公开工具调用，不读取 reasoning 文本；匹配 IK/FK、Jacobian、kinematics、least_squares 等标识，并先剔除长 base64 图像字符串。下面的实例直接包含 FK 和数值 IK 代码，因此可以确认实际发生了坐标/关节转换。
+
+| 格式 | 有运动学代码证据的轨迹 | 平均 shell 命令数 | 程序执行时间 s/轨迹 | 控制阶段 reasoning tokens/动作 | 控制阶段 output tokens/动作 |
+|---|---:|---:|---:|---:|---:|
+| EEF absolute | 2/60 | 1.6 | 0.4 | 124.6 | 231.6 |
+| EEF delta | 1/60 | 1.4 | 0.4 | 117.9 | 217.7 |
+| Joint absolute | 60/60 | 40.6 | 14.8 | 154.4 | 311.8 |
+| Joint delta | 60/60 | 42.9 | 16.3 | 157.3 | 314.2 |
+
+例如 joint_absolute 的 PlasticBottles 第 1 次轨迹，模型创建 `ik.py`，实现 FK，再用 `scipy.optimize.least_squares` 优化位置与姿态误差，随后执行 `python ik.py '[0.45,0,0.47]'`，把求出的关节角传给 `act`。joint_delta 同样构建 FK/IK 程序，再输出相对关节变化。
+
+这更支持“agent 能用程序构建动作接口适配层”。这些案例仍以空间位置/姿态为计算目标；不能因此断言模型直接、原生地在关节空间规划，也不能据此推广到未测过的机器人形态或任意动作接口。
+
+程序真正运行的时间平均只有约 15–16 秒/条 joint 轨迹，远小于两类接口之间的数百秒控制耗时差。额外程序构建、工具往返和模型生成是合理解释；现有日志不足以精确分解它们各自的因果贡献。
+
+## 2. joint 的代价持续存在，主要分界是 EEF/joint
+
+| 格式 | 首次动作平均耗时 s | 后续动作平均耗时 s |
+|---|---:|---:|
+| EEF absolute | 26.3 | 9.4 |
+| EEF delta | 23.9 | 9.5 |
+| Joint absolute | 55.5 | 14.9 |
+| Joint delta | 53.3 | 14.4 |
+
+后续动作先在每条轨迹内平均，再对 60 条轨迹平均。joint 不仅初始化更慢，后续决策仍更慢。按任务平均的每动作时间比较，20/20 个任务上 joint 两组合并都慢于 EEF 两组合并。
+
+同一空间内，absolute/delta 的合并每动作耗时只差约 2%–3%；joint 相对 EEF 则增加约 44%–45%。合并 joint/EEF 耗时比的任务分组 bootstrap 95% 区间为 [1.30, 1.60]。
+
+本实验限制的是模拟控制步数，没有给四组��同的真实时间或 token 预算。因此成功率接近，表示在允许更多在线计算时，joint 可以维持接近的任务表现；不能直接推出实时控制能力相同。
+
+## 3. 总分相近，任务偏好明显不同
+
+| 任务 | EEF abs | EEF delta | Joint abs | Joint delta |
+|---|---:|---:|---:|---:|
+| SpoonInMugTask | 0/3 | 0/3 | 2/3 | 2/3 |
+| BlockStackingSpecifiedOrderTask | 1/3 | 2/3 | 2/3 | 3/3 |
+| BananaThenRubiksCubeTask | 2/3 | 2/3 | 2/3 | 0/3 |
+| TakeSpatulaOffShelfTask | 2/3 | 1/3 | 0/3 | 1/3 |
+| FruitsOnionToPlateTask | 0/3 | 0/3 | 0/3 | 0/3 |
+
+勺子入杯是较明确的互补例子：joint 合计 4/6，EEF 0/6。取铲子则 EEF 3/6，joint 1/6。可以提出“接口/控制器改变可行姿态、接近路径及局部操作方式”的假设，但每格仅 3 次，不能据此确定某个格式稳定擅长某类操作。
+
+尤其需要注意：EEF 在环境端使用 IK 控制器、空间位置插值和 SLERP；joint 使用关节控制器和关节线性插值。因此实验同时改变了动作表示、轨迹插值与底层控制路径，不是只换 JSON 字段。
+
+## 4. delta 的共同信号是更少的撞桌事件
+
+| 格式 | GRIPPER_HIT_TABLE 总数 | 每 1000 控制步次数 | 出现过撞桌的轨迹 |
+|---|---:|---:|---:|
+| EEF absolute | 111 | 2.56 | 44/60 |
+| EEF delta | 72 | 1.56 | 36/60 |
+| Joint absolute | 102 | 2.16 | 40/60 |
+| Joint delta | 62 | 1.45 | 31/60 |
+
+相对 absolute，EEF delta 的按步归一化撞桌率下降约 39%，joint delta 下降约 33%。这与相对动作更便于局部修正的解释一致，但尚未证明该机制。任务分组 bootstrap 的 delta/absolute 撞桌率比区间：EEF [0.42, 0.82]，joint [0.43, 1.04]。后者跨过 1，证据相对更弱。
+
+这些是 benchmark 记录的事件次数，不是独立事故数；未将 TARGET_OBJECT_DROPPED 当成失败，因为放置时的主动释放也可能产生相关事件。
+
+## 5. 部分瓶颈跨格式保持不变
+
+洋葱放盘子 0/12，mustard 放左箱 1/12，白杯重定向 2/12。对于洋葱任务，各格式的结果都未满足目标物体抓取条件；只换动作接口未解决该问题。需要结合视频/逐步观察区分视觉定位、抓取几何、接触控制和评分判定，终止原因本身不足以确定根因。
+
+## 统计边界和下一步
+
+joint_delta 的成功率最高，但仅比 EEF absolute 多成功 3 条。以任务为分组单位，同时重采样四个格式的 20 个任务，进行 20,000 次 percentile bootstrap：差值 5 个百分点的 95% 区间为 [-11.7, 20.0] 个百分点。这不支持确定的格式胜负，也没有建立严格的统计等效性。区间只反映这组任务和重复的采样变异；不涵盖模型更新、提示词变化或其他机器人。
+
+最有信息量的后续实验：
+
+1. 为 joint 预置统一、经过校验的 IK helper，与当前自行构建 IK 的条件比较；分开量化工具构建与反复调用的成本，并尽量对齐 EEF 的控制器/插值。
+2. 统一真实时间或控制阶段 token 预算，再比较成功率，检验更多计算是否在补偿接口差异。
+3. 对勺子入杯、取铲子、堆叠等有交叉结果的任务增加重复；对洋葱任务做视频和逐步失败分析。
+
+## 完整任务结果
+
+| 任务 | EEF abs | EEF delta | Joint abs | Joint delta |
+|---|---:|---:|---:|---:|
+| BananaInBowlTask | 3/3 | 3/3 | 2/3 | 3/3 |
+| BananaThenRubiksCubeTask | 2/3 | 2/3 | 2/3 | 0/3 |
+| BananasInBinThreeTotalTask | 2/3 | 1/3 | 2/3 | 2/3 |
+| BlockStackingSpecifiedOrderTask | 1/3 | 2/3 | 2/3 | 3/3 |
+| BowlStackingRightOnLeftTask | 2/3 | 2/3 | 2/3 | 2/3 |
+| ClampInRightBinTask | 1/3 | 1/3 | 1/3 | 0/3 |
+| FoodPacking1BoxesTask | 2/3 | 1/3 | 1/3 | 0/3 |
+| FoodPacking1CansTask | 2/3 | 2/3 | 1/3 | 2/3 |
+| FruitsOnionToPlateTask | 0/3 | 0/3 | 0/3 | 0/3 |
+| MarkerInMugTask | 0/3 | 1/3 | 1/3 | 1/3 |
+| MustardInLeftBinTask | 0/3 | 0/3 | 0/3 | 1/3 |
+| OneBottleInSquarePailTask | 0/3 | 2/3 | 1/3 | 1/3 |
+| PlasticBottlesInSquarePailTask | 1/3 | 1/3 | 1/3 | 0/3 |
+| RedItemsInBinTask | 3/3 | 3/3 | 2/3 | 3/3 |
+| ReorientRedMugTask | 2/3 | 1/3 | 1/3 | 3/3 |
+| ReorientWhiteMugsTask | 0/3 | 1/3 | 0/3 | 1/3 |
+| RubiksCubeThenBananaTask | 2/3 | 2/3 | 3/3 | 2/3 |
+| SmallPumpkinInBinTask | 1/3 | 1/3 | 2/3 | 2/3 |
+| SpoonInMugTask | 0/3 | 0/3 | 2/3 | 2/3 |
+| TakeSpatulaOffShelfTask | 2/3 | 1/3 | 0/3 | 1/3 |
+
+## 文件与复现
+
+- [比较图](comparison.png) / [PDF](comparison.pdf)
+- [汇总](summary.csv)、[逐任务结果](per_task.csv)、[逐轨迹数据](episodes.csv)、[bootstrap](bootstrap.json)
+- `analyze.py` 从原始日志抽取数据，`make_report.py` 生成报告与图表；无需网络或模型调用。
+- 逐轨迹 CSV 的 result_path / summary_path / events_path 均相对上一级 `robolab_exploration` 目录，可以回查来源。
+- 工具统计只包含最后一次动作开始以前的已完成 shell 命令/文件修改，排除任务结束后总结。运动学标识匹配是可审计的日志代理指标，不把任意 shell 调用都算成 IK。

harness_robolab_exploration/action_format_analysis_20261029/summary.csv

@@ -0,0 +1,5 @@
+format,episodes,successes,success_rate,mean_score,mean_harness_seconds,median_harness_seconds,mean_wall_seconds,median_wall_seconds,mean_decision_count,median_decision_count,mean_executed_control_steps,median_executed_control_steps,mean_seconds_per_decision,median_seconds_per_decision,mean_first_decision_raw_seconds,median_first_decision_raw_seconds,mean_later_decision_raw_seconds,median_later_decision_raw_seconds,mean_command_count,median_command_count,mean_command_seconds,median_command_seconds,mean_command_failures,median_command_failures,mean_first_action_commands,median_first_action_commands,mean_kinematics_commands,median_kinematics_commands,pooled_seconds_per_decision,control_reasoningOutputTokens_per_decision,control_outputTokens_per_decision,control_inputTokens_per_decision,control_cachedInputTokens_per_decision,kinematics_episodes,command_episodes,usage_complete_episodes,rate_limit_count,excluded_seconds,event_GRIPPER_FULLY_CLOSED_total,event_GRIPPER_FULLY_CLOSED_per_1000_control_steps,event_GRIPPER_HIT_OBJECT_total,event_GRIPPER_HIT_OBJECT_per_1000_control_steps,event_GRIPPER_HIT_TABLE_total,event_GRIPPER_HIT_TABLE_per_1000_control_steps,event_MULTIPLE_OBJECTS_GRABBED_total,event_MULTIPLE_OBJECTS_GRABBED_per_1000_control_steps,event_OBJECT_BUMPED_total,event_OBJECT_BUMPED_per_1000_control_steps,event_OBJECT_MOVED_total,event_OBJECT_MOVED_per_1000_control_steps,event_OBJECT_OUT_OF_SCENE_total,event_OBJECT_OUT_OF_SCENE_per_1000_control_steps,event_TARGET_OBJECT_DROPPED_total,event_TARGET_OBJECT_DROPPED_per_1000_control_steps,event_WRONG_OBJECT_GRABBED_total,event_WRONG_OBJECT_GRABBED_per_1000_control_steps,metric_ee_isj_median,metric_ee_path_length_median,metric_ee_sparc_median,metric_ee_speed_max_median,metric_ee_speed_mean_median,metric_joint_isj_median,metric_joint_rmse_mean_median,metric_joint_sparc_mean_median
+eef_abs,60,26,0.43333333333333335,0.5192777787645658,516.8727407898754,497.14037535339594,774.6068216005962,765.8049709349871,48.5,59.5,723.1666666666666,886.5,9.959478411300362,9.667173821917352,26.319155921166143,25.52889996767044,9.434869730595477,9.207835326249064,1.5666666666666667,1.0,0.3827333333333333,0.044,0.016666666666666666,0.0,0.38333333333333336,0.0,0.18333333333333332,0.0,10.657169913193307,124.56391752577319,231.5676975945017,73949.82130584192,72174.83436426117,2,38,55,0,104.05716126412153,316,7.282784051624798,81,1.866789582853192,111,2.558193132058078,13,0.29960820465545057,212,4.885918414381194,19,0.43788891449642775,15,0.34570177460244295,237,5.462088038718599,107,2.466005992164093,1168.261859156281,4.442672787242304,-6.006636400263975,0.4318149536848068,0.09893323108553886,93777.52650847801,1.088475802418623,-10.831522743923205
+eef_delta,60,27,0.45,0.5137777785460155,538.9693245867888,501.48883639648557,810.6937252142777,777.3950718864799,51.5,57.0,768,841.5,9.895407181879541,8.989418027177454,23.872719909995794,22.35639102384448,9.542809433625212,8.732657874953107,1.4333333333333333,0.0,0.39071666666666666,0.0,0.1,0.0,0.03333333333333333,0.0,0.15,0.0,10.465423778384249,117.93236245954692,217.74822006472493,70133.18220064725,68497.2640776699,1,22,56,0,109.74538015574217,355,7.703993055555555,75,1.6276041666666667,72,1.5625,8,0.1736111111111111,193,4.188368055555555,12,0.2604166666666667,11,0.2387152777777778,264,5.729166666666667,100,2.170138888888889,961.5724553543773,4.123991612958613,-6.005318702290152,0.35049647092819214,0.08617227151989937,58301.11492231472,1.0428124212436747,-9.064633373900165
+joint_abs,60,26,0.43333333333333335,0.5221111118793488,814.1084779527039,782.966099716723,1075.2660846352578,1043.2855218984187,52.7,57.5,786.15,856.0,15.602470330608954,15.158311486791707,55.50223632020255,52.641883317381144,14.901536943094664,14.483736114597868,40.63333333333333,41.0,14.750816666666667,14.621500000000001,1.1166666666666667,1.0,4.083333333333333,4.0,34.11666666666667,36.5,15.447978708779958,154.41018342821,311.8266919671094,117144.1385199241,115230.95351043643,60,60,55,0,615.6098177134991,386,8.183340753460959,68,1.4416247959464903,102,2.1624371939197355,10,0.21200364646271916,165,3.498060166634866,26,0.5512094808030699,25,0.5300091161567979,281,5.9573024656024085,127,2.6924463100765332,1121.663489241138,4.519282504114831,-6.407852632349149,0.40095384418964386,0.087232306599617,45753.40431577913,1.648703877735477,-9.515894530665093
+joint_delta,60,29,0.48333333333333334,0.5388888895511628,719.579712490365,592.425684697926,956.9595386667798,799.6019495166838,47.916666666666664,42.0,713.7666666666667,620.0,15.237269206925067,14.30836560609718,53.25075703871747,51.18401175364852,14.444908454323748,13.518925815634429,42.9,38.5,16.277116666666668,13.1735,0.9833333333333333,1.0,3.6333333333333333,4.0,32.233333333333334,27.5,15.017315738929355,157.30852173913044,314.1780869565217,108425.58643478261,106397.8775652174,60,60,55,0,545.2625974863768,371,8.662961752206604,71,1.6578713865408863,62,1.4477186755709148,10,0.23350301218885725,175,4.086302713305002,6,0.14010180731331434,7,0.16345210853220007,264,6.1644795217858315,127,2.965488254798487,990.1887599454867,3.6797591340739393,-6.547230447607256,0.40584859251976013,0.08881811425089836,34640.860611723176,1.6426346314484443,-8.741695838874623

harness_robolab_exploration/embo_astra/assignments.tsv

@@ -0,0 +1,5 @@
+gpu	port	estimated_seconds	tasks
+0	8010	14395.854	PlasticBottlesInSquarePailTask OneBottleInSquarePailTask BananasInBinThreeTotalTask BowlStackingRightOnLeftTask
+1	8011	13334.856	FruitsOnionToPlateTask BlockStackingSpecifiedOrderTask ReorientRedMugTask BananaThenRubiksCubeTask FoodPacking1CansTask
+2	8012	14549.234	MarkerInMugTask SpoonInMugTask ClampInRightBinTask FoodPacking1BoxesTask RubiksCubeThenBananaTask BananaInBowlTask
+3	8013	13272.163	SmallPumpkinInBinTask ReorientWhiteMugsTask TakeSpatulaOffShelfTask RedItemsInBinTask MustardInLeftBinTask

harness_robolab_exploration/embo_astra/batch_manifest.json

@@ -0,0 +1,38 @@
+{
+  "generations": [
+    {
+      "gpus": [
+        0,
+        1,
+        2,
+        3
+      ],
+      "path": "."
+    }
+  ],
+  "runs": 3,
+  "schema_version": 1,
+  "task_costs": {
+    "BananaInBowlTask": 425.86899999999997,
+    "BananaThenRubiksCubeTask": 719.5473000000001,
+    "BananasInBinThreeTotalTask": 589.033,
+    "BlockStackingSpecifiedOrderTask": 905.3386,
+    "BowlStackingRightOnLeftTask": 451.9177,
+    "ClampInRightBinTask": 888.4855,
+    "FoodPacking1BoxesTask": 721.0428,
+    "FoodPacking1CansTask": 467.5319,
+    "FruitsOnionToPlateTask": 1483.3265,
+    "MarkerInMugTask": 1356.3095,
+    "MustardInLeftBinTask": 503.56759999999997,
+    "OneBottleInSquarePailTask": 783.6646,
+    "PlasticBottlesInSquarePailTask": 2974.0027,
+    "RedItemsInBinTask": 766.2022,
+    "ReorientRedMugTask": 869.2076999999999,
+    "ReorientWhiteMugsTask": 1090.1975,
+    "RubiksCubeThenBananaTask": 522.5474,
+    "SmallPumpkinInBinTask": 1174.1926,
+    "SpoonInMugTask": 935.4904,
+    "TakeSpatulaOffShelfTask": 889.8943
+  },
+  "task_set": "sub"
+}

harness_robolab_exploration/embo_astra/gpu_0/harness/20261030T160311.809061Z_eval.episodes.jsonl

@@ -0,0 +1,12 @@
+{"schema_version": 4, "episode_id": "4d9fcbbd729d4a87b1b67630849a780e-0", "episode_index": 1, "backend": "codex", "model": "gpt-6-astra", "reasoning_effort": "medium", "async_inference": false, "plan": false, "action_format": "eef_absolute", "async_tail_steps": 5, "instruction": "Put all the small plastic bottles in the square pail", "max_steps": 2700, "control_dt": 0.06666666666666667, "started_at": "2026-10-30T16:04:57.164931+00:00", "harness_seconds": 4778.401259250939, "finish_seconds": null, "decision_count": 338, "issued_control_steps": 2700, "executed_control_steps": 2700, "control_steps_complete": true, "executed_vla_steps": 0, "vla_delegation_ratio": 0.0, "tokens": {"input_tokens": 21793187, "output_tokens": 51065, "total_tokens": 21844252, "cached_input_tokens": 21601211, "cache_write_input_tokens": 191424, "reasoning_output_tokens": 27603}, "usage_complete": false, "api_attempt_count": null, "api_retry_count": null, "ended_at": "2026-10-30T18:21:59.425531+00:00", "status": "ended", "end_reason": "native_terminal", "simulated_seconds": 180.0, "raw_wall_seconds": 8191.278087668121, "raw_harness_seconds": 7270.642206177115, "raw_finish_seconds": 30.980000495910645, "timing_status": "complete", "finish_timing_status": "partial", "excluded_seconds": 2492.2409469261765, "finish_excluded_seconds": 0.0, "rate_limit_count": 0, "finish_rate_limit_count": 0, "network_timing_status": "complete", "network_wait_seconds": 2492.2409469261765, "network_excluded_seconds": 2492.2409469261765, "rate_limit_timing_status": "complete", "rate_limit_wait_seconds": 0, "rate_limit_excluded_seconds": 0.0, "finish_network_timing_status": "partial", "finish_network_wait_seconds": 0, "finish_network_excluded_seconds": 0.0, "finish_rate_limit_timing_status": "partial", "finish_rate_limit_wait_seconds": 0, "finish_rate_limit_excluded_seconds": 0.0, "wall_seconds": 5699.037140741944, "scorer_checks": 0, "scorer_low_score_returns": 0, "scorer_seconds": 0.0, "scored_action_seconds": 0.0, "codex_finish": "timeout"}
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