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LocalModels-ACT-bimanual-rollouts

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Robotics
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reinforcement-learning
grpo
mujoco
roboeval
bimanual
rollouts
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metadata 
license: apache-2.0
task_categories:
  - robotics
tags:
  - reinforcement-learning
  - grpo
  - mujoco
  - roboeval
  - bimanual
  - rollouts
  - robotics
pretty_name: LocalModels ACT-bimanual GRPO rollouts
size_categories:
  - 100K<n<1M

LocalModels / ACT-bimanual: GRPO rollouts

Rollouts saved by team LocalModels during the CoRL 2026 RoboEval challenge: 29 GRPO fine-tuning runs of ACT policies on six bimanual tasks, 389,856 episodes (318,256 successes, 75.8 million steps, 3.96 GB). Each episode is stored as its environment seed plus every executed action, about 10 kB instead of about 40 MB rendered. With pinned BLAS the simulator is deterministic, so an episode replays exactly and its camera frames can be re-rendered.

The final entry ships one learned model, LiftPot: LiftPot/grpo_02 is the GRPO stage that produced it. The other runs (including LiftPot/grpo_01) fine-tuned ACT models that the final entry does not use.

Runs

Runs are numbered per task in the order of their GRPO round numbers. Rollouts use training seeds (10,000 and above): 8 noisy rollouts per start state, Gaussian noise (sigma 0.2 or 0.3) on the normalized actions.

Task Run Rounds Episodes Success Notes
LiftPot grpo_01 31000-31116 11,232 75% restart from GRPO v1 round 1150; was lp_l1
LiftPot grpo_02 37000-37199 19,200 84% trained the shipped LiftPot model (snapshot round 37100: rounds 37000-37099, 9,600 episodes); was lp_l2
PackBox grpo_01 18045-18199 14,880 81% was gpb9500_t1
PackBox grpo_02 20000-20086 8,352 78% was pb9500b_v2_5090
PackBox grpo_03 30000-30133 12,864 84% was gpb_p1
PackBox grpo_04 41000-41199 19,200 82% task-progress reward bonus; was pbtp_5090
PackBox grpo_05 43000-43199 19,200 91% was pb_p3_5090
PackBox grpo_06 44000-44261 16,768 90% was pb_p4_5090
PackBox grpo_07 48000-48499 32,000 92% was pb_p5_5090
PackBox grpo_08 49000-49249 24,000 94% was pb_p6
PackBox grpo_09 50000-50316 30,432 94% was pb_p7_5090
PickSingleBookFromTable grpo_01 34000-34199 19,200 82% was psb_k1
PickSingleBookFromTable grpo_02 46000-46156 10,048 83% was psb_k2
RotateValve grpo_01 45000-45089 5,760 86% was rv_r2
RotateValve grpo_02 47000-47299 28,800 88% was rv_r3
StackSingleBookShelf grpo_01 23000-23024 2,400 61% action scale 0.85; was ssbs160_v2
StackSingleBookShelf grpo_02 28000-28009 960 76% action scale 0.85; was ssbsm1_v2
StackSingleBookShelf grpo_03 35000-35199 19,200 88% action scale 0.85; was ssbsm2_v2
StackSingleBookShelf grpo_04 36000-36199 19,200 90% action scale 0.85; was ssbsm2_v2b
StackTwoBlocks grpo_01 16045-16199 14,880 58% was s2bbr_v2b
StackTwoBlocks grpo_02 17060-17199 8,960 60% was s2bbr_a3
StackTwoBlocks grpo_03 21075-21149 7,200 58% was s2bbr_v2b_f5090
StackTwoBlocks grpo_04 22075-22174 9,600 60% was s2bbr_a4
StackTwoBlocks grpo_05 25075-25086 1,152 59% privileged value baseline; was s2bbr_a5
StackTwoBlocks grpo_06 26000-26161 15,552 63% privileged value baseline; was s2bbr_a5b
StackTwoBlocks grpo_07 27000-27079 7,680 53% was s2bbr_a6
StackTwoBlocks grpo_08 29000-29054 5,280 54% was s2bbr_a6b
StackTwoBlocks grpo_09 32000-32057 5,568 57% was s2bbr_a7
StackTwoBlocks grpo_10 33000-33002 288 61% was s2b_a8

Notes: ShelfBook runs executed the policy's actions scaled by 0.85 (the stored actions are the executed ones). All StackTwoBlocks rewards include shaping terms computed from simulator positions, and two of its runs used a value baseline from simulator state; the stored data are still seeds and actions only.

Format 

<Task>/<run>/log.jsonl                one line of GRPO metrics per round
<Task>/<run>/successes/it<N>.npz      round N, episodes that succeeded (+ it<N>.json)
<Task>/<run>/failures/it<N>.npz       round N, all other episodes      (+ it<N>.json)
  • it<N>.npz: actions (float32, steps x 14: end-effector deltas dp_L dr_L dp_R dr_R g_L g_R at 20 Hz) and offsets (episode k is actions[offsets[k]:offsets[k+1]]).
  • it<N>.json: one record per episode: seed, steps, term (how it ended), success, tp (task progression), R (GRPO reward), iter, prefix_t (actions replayed from a logged episode before the rollout; 0 for a fresh start).

To replay an episode: env.reset(seed=seed), then step every stored action (the first is a one-step open-gripper no-op). Exact replay needs RoboEval at tag corl2026-challenge, MuJoCo 3.3.3 and pinned BLAS (OPENBLAS_CORETYPE=Haswell OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 MKL_CBWR=COMPATIBLE).

License

Apache-2.0. No demonstration data or rendered frames are included.