podolinsky/pi0.5-libero-10-features

pi0.5 LIBERO-10 prefix features — normal + occluded

Aligned rollouts of the stock openpi pi05_libero policy on the LIBERO libero_10 suite, collected in two matched scene variants:

• normal — the original LIBERO libero_10 tasks / initial states.
• occluded — the filename- and initial-state-matched libero_10_occluded suite from LIBERO-Occ, which adds scene-induced occlusion to the same tasks so the effect of occlusion can be read off pairwise.

10 tasks × 2 variants × 25 initial states = 500 episodes (25 normal + 25 occluded per task). Every π0.5 inference, executed action, and video frame in an episode is joinable by array row.

Benchmarks

• LIBERO — Liu et al., Benchmarking Knowledge Transfer for Lifelong Robot Learning, arXiv:2306.03310
• LIBERO-Occ — Li et al., Evaluating and Improving Vision-Language-Action Models under Scene-Induced Occlusion via Viewpoint Imagination, arXiv:2606.10862
• π0.5 — Physical Intelligence, a Vision-Language-Action Model with Open-World Generalization, arXiv:2504.16054

Which π0.5 features

π0.5 runs a prefix forward pass through its PaliGemma backbone (SigLIP vision + Gemma-2B LM) over the image and language tokens; the resulting last-layer hidden states fill the KV cache that then conditions the flow-matching action expert. openpi normally discards those hidden states — here they are captured, once per policy inference (i.e. every replan_steps = 5 control steps), raw and per-token (no pooling), as float16:

key	shape	tokens
base_image	(256, 2048)	base RGB camera, 224×224 → 16×16 SigLIP patches
wrist_image	(256, 2048)	left wrist RGB camera, same
language	(L, 2048)	instruction tokens (padded to 200; see language_mask)
language_mask	(L,) bool	real vs padding for language

2048 is the Gemma-2B hidden width. The always-zero right-wrist camera slot is dropped. These are the frozen-backbone representation before the action expert — the input to any perception / failure probe.

Layout

<scene_variant>/<NN>_<task_stem>/ep<NNN>/
    rollout.json    metadata + per-policy / per-control clock records
    rollout.npz     the arrays below
    rollout.mp4     agentview video, 20 fps, one frame per control step
    wrist.mp4       wrist video, same timing
manifest.csv        one row per episode

rollout.npz

Features: base_image, wrist_image, language, language_mask — each stacked over the n_policy inferences.

Actions: predicted_action_chunks (n_policy, 10, 7), predicted_chunk_len, executed_actions (n_control, 7).

Clocks, per control step (n_control,): control_step, sim_step (includes the settle/wait steps, which are not in the video), policy_step (which inference produced this step), chunk_index (position within that predicted chunk), video_frame_id (== control_step).

Scalars: success, replan_steps, n_policy, n_control, img_tokens, hidden, control_hz (20).

Alignment contract

executed_actions[t] == predicted_action_chunks[policy_step[t], chunk_index[t]], video frame t is control step t, and each policy_step maps to a contiguous block of control steps. Every episode passed these checks at collection time (n_control == 520 on failure = the step cap was hit).

Labels

Not included in this drop. Gemini 3-second action captions and a two-pass failure localizer are produced separately and land as labels.json + labels.npz next to each rollout.*, leaving the files above untouched.

Provenance

Collected with scripts/semantic_failure/ in the 12-Visual-Occlusion-Reasoning project against a local feature-serving pi05_libero server. replan_steps = 5, num_steps_wait = 10, seed 7, policy image size 224.