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alexhegit/so101-simstudio-lab01-pnp-act-state6
so101-simstudio-lab01-pnp-act-state6 is a robotics model from alexhegit. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for lerobot. The card lists the license as apache-2.0.
ACT policy fine-tuned on expert demonstrations from SO-101 SimStudio (MuJoCo, leader-arm teleop).
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From the Hugging Face model README
ACT policy fine-tuned on expert demonstrations from SO-101 SimStudio (MuJoCo, leader-arm teleop).
Training data: alexhegit/so101-simstudio-lab01-pnp (on-disk observation.state is still 15-D: 6 joint pos + 6 vel + 3 EE). This checkpoint was trained on the first 6 dims only (joint .pos), matching official real LeRobot SO-101 (so_follower) proprioception.
Why 6-D: so the policy input matches real-robot IL and is easier to sim2real / merge with real datasets. Extra sim channels (velocity, end-effector XYZ) are not available on the stock real follower. Units (radians vs degrees, gripper scale) still need a separate alignment.
The 15-D ACT reference (same 50K schedule, pos+vel+ee) is alexhegit/so101-simstudio-lab01-pnp-act.
| Item | Value |
|---|---|
| GPU | AMD Instinct MI300X (DORobot) |
| Batch / steps | 128 / 50 000 |
| Train loss | 0.054 |
| Sim2sim eval | full-range, reset_arm: follow, sync, EGL, n_action_steps=50 → 29/50 (58%) |
| 15-D ACT same protocol | 32/50 (64%) — same level at n=50 |
Wall time ~17 h. Checkpoint 050000 / last.
| Resource | Link |
|---|---|
| SimStudio repo | rocPAI-Forge/so101-simstudio |
| Lab 01 walkthrough | labs/lab01_pnp/lab01_pnp.md |
from lerobot.policies.act.modeling_act import ACTPolicy
policy = ACTPolicy.from_pretrained("alexhegit/so101-simstudio-lab01-pnp-act-state6")
hf download alexhegit/so101-simstudio-lab01-pnp-act-state6 \
--local-dir ./outputs/hub/lab01_pnp_act_state6
Sim2sim eval: labs/lab01_pnp/configs/rollout_act.yaml (same YAML as 15-D ACT; rollout already sends joint .pos).