LIBERO-CF-Mini benchmark card

Summary

LIBERO-CF-Mini v0.1 is WAMProbe's Tier-1 paired simulator benchmark. It starts several candidate action sequences from an exactly restored simulator state and records their state and RGB futures. The benchmark measures whether a model distinguishes action consequences; it is not a LIBERO policy leaderboard and its diagnostic branches are not expert actions.

The fixed task manifest covers four intentionally different families:

Key LIBERO suite Family Fixed task
spatial-task0 libero_spatial spatial relation black bowl between two objects → plate
object-task0 libero_object object identity alphabet soup → basket
goal-task0 libero_goal articulated goal open the middle drawer
long-horizon-task0 libero_10 long-horizon composition two objects → basket

The source selection, task text, BDDL/init-state filenames and hashes are pinned in configs/benchmarks/libero_cf_mini_v0.1.json. The manifest's SHA256 is 1660ab49ec0e5aac58f4801f0ec9a12326053f21f5f1974dd3b6ea63260f78c3.

Upstream data and license

  • Source: LIBERO
  • Revision: 8f1084e3132a39270c3a13ebe37270a43ece2a01
  • Upstream code, BDDL assets and fixed init states: MIT license
  • WAMProbe-generated metadata: Apache-2.0
  • No LIBERO demonstrations are required or redistributed by this benchmark slice.

Users redistributing generated image/state bundles should retain both projects' license and provenance notices. The runner rejects an upstream commit, task description, BDDL, init-state file, shape or dtype that differs from the manifest.

Intervention protocol

Each task uses init state 0, 30 no-op settling steps, two 256×256 RGB cameras and an eight-step branch horizon. Four constant commands are applied from one shared snapshot:

  1. open-gripper no-op;
  2. positive end-effector X;
  3. negative end-effector X;
  4. stationary gripper close.

The snapshot contains MuJoCo mjSTATE_INTEGRATION plus robosuite clock, controller, observable, gripper and Python/NumPy RNG side state. Every task is checked with two independent restores, a repeated no-op branch, and forward/reverse branch execution. All state and image references carry size and SHA256 metadata.

Verified v0.1 pilot

The four-task, 16-branch, 128-post-action-frame run completed on 2026-07-15. Every exact restore check passed and the maximum integration-state error was 0.0.

Task Snapshot SHA256 Artifact SHA256 EEF separation Object-state separation No-op drift
spatial 22f220ce…e36 73014e3d…ae97 0.0395949 0.0979067 0.0000247
object d73e2f59…d8d3 2bfda04f…db1 0.0395781 0.1135034 0.0000762
goal aadc9f6f…bef9 9e34e5df…bac3 0.0395915 0.0873014 0.0000340
long-horizon ae5737d2…a082 014aea0c…df3a 0.0396259 0.1234343 0.0000605

Full hashes and values are available in libero_cf_mini_verified_v0.1.json. A second run verified the artifact JSON, snapshot sidecars and every PNG by size and SHA256, then reported four cache hits without opening a simulator.

Reproduction

Follow the isolated environment instructions in environments/libero/README.md, then run:

environments/starwam/.venv/bin/python environments/libero/generate_cf_pilot.py \
  --gpu-index 0 --horizon 8 --run-dir runs/libero-cf-mini-v0.1

The command processes all four tasks by default and writes a structured failure record to index.json after every task. Repeat --task-key to reproduce a smaller subset. Valid existing artifacts resume as cache hits; use --force only to regenerate them.

Suitable and unsuitable uses

Suitable uses include snapshot/restore validation, action-condition diagnostics, short horizon state/video comparisons and model-integration smoke tests. Unsuitable uses include reporting LIBERO task success, comparing policies, claiming task completion, or aggregating raw object-state distances across tasks whose state vector dimensions differ.

Limitations

  • The four fixed commands are causal probes, not constraint-aware expert trajectories.
  • Eight steps are too short for the selected sparse-reward tasks; all verified return spreads and success rates are zero. This is reported as a limitation, not hidden.
  • The current branch set probes X translation and gripper closure only.
  • One init state per family is insufficient for paper-level statistical claims.
  • RGB similarity, keypoint metrics, real-WAM predictions and closed-loop control utility are separate evaluation stages and are not established by this data-generation run.