---
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.

- Models and the official run: [Sa74ll/LocalModels-ACT-bimanual](https://huggingface.co/Sa74ll/LocalModels-ACT-bimanual)
- Code and report: [github.com/Sa74ll/RoboevalChallenge](https://github.com/Sa74ll/RoboevalChallenge)

## 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.