davidwdw/b1k

GPT-6 generated BEHAVIOR-1K policies — Task 00, Task 01 & Task 02

Collected from eai on 2026-09-14. Both policies are standalone code (no Codex / LLM / API calls at run time) evaluated with the official BEHAVIOR v3.9.2 evaluator.

Task	Source on eai	Run set	Runs	Successes	Mean final Q
01 picking_up_trash	/home/eai/yixin/gpt6_b1k	final_prior_frozen20 (instances 301–320, seed 0)	20	13	0.767
01	rerun on this laptop (2026-09-14)	rerun_local_20260914 (same frozen code, + videos) — REPORT	20	14	0.800
02 putting_away_Halloween_decorations	/home/eai/yixin/code/gpt6cap	instances301-320_seed0 (skip 312)	19	0	0.293
02		instance312_seeds0-4 (seed 5 interrupted, no result)	5	0	0.657
02		repeat_20260912_6inst (301, 303, 308, 317, 318, 320)	6	0	0.238

Per-run numbers: scores.csv.

Layout

gpt6_b1k/
  README.md  scores.csv
  SHA256SUMS                 # checksum of every file below (sha256sum -c SHA256SUMS)
  MANIFEST.tsv               # path, sha256, original eai path (or how a file was derived)

  task01_picking_up_trash/
    code/                    # exact frozen snapshot that was evaluated (identical to repo head)
                             #   gpt6_b1k/ policy, config/, assets/ (models + scene prior), third_party/,
                             #   run.py, env.sh, tests/, README/EVALUATION/EXPERIMENTS/PRIOR_DESIGN.md
    final_prior_frozen20/
      summary.json manifest.json verification.json remote_verification.json launcher_final.py logs/
      instance_<id>/
        result.json          # official evaluator output
        traj/actions.jsonl   # per-step actions (split from batch file)
        traj/events.jsonl    # policy events (split from batch file)
        traj/observations/<episode>_<step>/   # sampled RGB + linear depth (.npy) for 3 cameras + observation.json
      instance_304/interrupted_attempt_batch000/traj/   # 304 attempt killed by transport failure (no score)
      raw_batches/batch_000, batch_001/   # original unsplit actions/events, evaluator/policy logs, command.json
    rerun_local_20260914/    # rerun on cesar-ThinkPad-P1-Gen-7 (RTX 3000 Ada), identical code/evaluator, video on
      REPORT.md summary.json manifest.json logs/{launcher.log,vram.csv} source/ raw_batches/batch_000/
      instance_<id>/ result.json  video.mp4  traj/{actions,events}.jsonl  traj/observations/
    earlier_runs/            # development runs in their original layout (partial; contain the only Task 01 videos)
      prior_frozen20_18/ visual_frozen20_16/ validation_pack09/ validation_floor10/

  task02_putting_away_halloween_decorations/
    code/                    # r21 policy: source/ (pipeline + scripts), reference/, launch_policy.py,
                             #   evaluate_seed.py, run_centre*.sh, run.sbatch, suites, batch_tools/, batches/,
                             #   checksum manifests, README/CODE_LAYOUT/BATCH_RUNS.md
    instances301-320_seed0/  # + RESULTS.md, FAILURE_ANALYSIS.md, FINAL_REPORT.md, summary.json, previews/
      instance_<id>/ result.json  video.mp4  traj/{actions,events}.jsonl  logs/{evaluator,policy}.log exit_code.txt
    instance312_seeds0-4/seed_<nn>/     # same per-run layout; seed_05 has logs/traj only
    repeat_20260912_6inst/instance_<id>/seed_000/attempt_001/   # same per-run layout (+ command.json, run_status.json)
                             # batch-level code/ snapshot, manifest.json, REPORT.md, FINAL_REPORT.md

Notes

• Task 01 local rerun (rerun_local_20260914): same 673 frozen files and evaluator hashes as the eai run, re-run here with --write-video; 19/20 instances match eai's Q exactly, 318 went 0.333 → 1.0 (different can order). Launcher: ~/code/gpt6_b1k/runs/launch_frozen_local.sh; files added later are appended to SHA256SUMS/MANIFEST.tsv by organize_rerun.py.
• Task 01 eai final run has no videos. Video encoding was off (official default). Only sampled RGB-D observations exist; the 8 Task 01 videos are in earlier_runs/*/batch_000/evaluation/videos/.
• Task 01 trajectory split. The evaluator ran all instances in one process per batch, so the policy wrote one actions.jsonl/events.jsonl per batch. Even episode numbers map to instances in order (batch_000: 2→301, 4→302, 6→303, 8–10→interrupted 304; batch_001: 2→304 … 34→320). Odd episodes are reset-only and were assigned to the following instance. Line counts of the split files sum exactly to the originals, which remain in raw_batches/.
• Task 02 raw sensor observations were not collected. They exist only on Centre (/home/david/r21-seed10/runs/...).
• Original Markdown reports were copied unchanged. Their links point to absolute eai paths / old folder names; use MANIFEST.tsv to map them.
• Renamed per-run files: evaluation/json/*.json → result.json, evaluation/videos/*.mp4 → video.mp4, policy/*.jsonl → traj/, logs → logs/.
• Not copied: gpt6_b1k debug/ (18 GB dev lab sessions), appdata/ (9.2 GB Isaac cache), observations of Task 01 earlier runs (~4.5 GB), gpt6cap official/ (565 MB unmodified BEHAVIOR v3.9.2 export; its checksums are in task02_.../code/OFFICIAL_SHA256SUMS), __pycache__, .sync.lock.
• index.html is the results viewer (regenerate with python3 build_index.py).
• Integrity: every copied file was verified against SHA256 computed on eai before reorganizing; SHA256SUMS verified again after.

Task 00 collection — 2026-09-18

Task 00 · turning_on_radio now contains the full 4080 production export: 300 instances (259 successes, 41 failures), both original LeRobot v3 datasets, frozen source and matching official evaluator, all original attempts and audit logs, and the Chinese distribution report. Open its results index. All copied files were verified against source SHA256. The generated depth videos retain the documented lossy encoding difference from official human demonstrations. Original Task 01/02 files are unchanged.

Within Task 00, code/ holds source; aligned_20260918/ holds per-instance results; datasets/task00_all_v1/ and datasets/task00_success_v1/ preserve their training dataset structures. Task 00 has its own summary.json and HTML index; the older root scores.csv remains the Task 01/02 score table.