RACE_gauss_liberoplus

RACE gauss (pi0.5 + Gaussian transition-proximity) — LIBERO-Plus best checkpoints

Two eval-only checkpoints of the gauss (gauss_timing) method, selected as the best step on the full LIBERO-Plus benchmark (arXiv:2510.13626, 10,030 perturbed variants, 1 trial/variant, seed 7, leaked task.language prompt as on the leaderboard):

"Zero-shot" = trained only on clean LIBERO and evaluated on LIBERO-Plus without seeing its perturbations (comparable with the leaderboard's pi0 53.6 / pi0-Fast 61.6 rows). "SFT" = post-trained on the benchmark's own training set (14,347 episodes, 100k steps at batch 128), i.e. an in-domain number comparable with OpenVLA-OFT+ (79.6) / SRPO (82.1).

Per-dimension success rate

Per suite (zero-shot / SFT): spatial 87.9 / 86.6, object 87.5 / 89.3, goal 80.5 / 79.8, libero_10 79.4 / 81.2.

Robot-init protocol: _initstate_N (N≠0) variants use reset() only (no set_init_state), so the perturbed initial pose is preserved. Sensor noise is applied to the agentview camera only (benchmark behaviour).

Layout

pi05_libero_gauss_h10/gauss/10000/                # zero-shot best
  model.safetensors
  metadata.pt
  assets/physical-intelligence/libero/norm_stats.json
pi05_libero_plus_gauss_h15/gauss/100000/          # SFT best
  model.safetensors
  metadata.pt
  assets/Sylvest/libero_plus_lerobot/norm_stats.json
assets/gauss_timing/libero_cps.npz                # serving-time load dependency (zero-shot config)
assets/gauss_timing/libero_plus_cps.npz           # serving-time load dependency (SFT config)

Optimizer states are NOT included.

Serving (openpi fork with gauss_timing)

# zero-shot best
python scripts/serve_policy.py --port 8000 --env LIBERO \
  policy:checkpoint --policy.config pi05_libero_gauss_h10 \
  --policy.dir <download_root>/pi05_libero_gauss_h10/gauss/10000

# SFT best
python scripts/serve_policy.py --port 8000 --env LIBERO \
  policy:checkpoint --policy.config pi05_libero_plus_gauss_h15 \
  --policy.dir <download_root>/pi05_libero_plus_gauss_h15/gauss/100000

Notes:

• Config names are defined in the RACE repo's src/openpi/training/config.py; serving is observation-only (the timing head predicts b internally — no changepoint inputs at inference).
• assets/gauss_timing/libero_cps.npz / libero_plus_cps.npz must exist relative to the server's CWD (repo root). They are loaded at policy build time but unused for inference.
• Replan convention: replan = horizon (h10→replan10, h15→replan15).
• The full 1k-step clean-LIBERO sweeps are in SeonghoonYu/RACE_gauss_libero.