arrow-hf/smolvla-robotwin-move-pillbottle-pad-50ep-multi

SmolVLA RoboTwin move_pillbottle_pad (50 ep, MULTI-instruction)

SmolVLA policy fine-tuned on 50 demonstration episodes of the `move_pillbottle_pad` task from RoboTwin 2.0 (demo_clean config), with per-episode random language instructions sampled from RoboTwin's 100 instruction variations (seed=42 for reproducibility).

This is the multi-instruction counterpart to `arrow-hf/smolvla-robotwin-move-pillbottle-pad-50ep` (which uses a single fixed instruction).

Task

• —Robot: Agilex dual-arm, end-effector control (16D state, 16D action)
• —Cameras: 3 RGB streams — dual_cam_global, cam_wrist_65, cam_wrist_75 (240×320, D435)
• —Control rate: ~30 Hz (LeRobot metadata is 10 Hz; underlying RoboTwin sim ~30 Hz, used consistently for train/eval)
• —Instructions: 50 unique sentences (one per episode), examples:
• —"Use the left arm to place the object into the basket"
• —"Pick the item up and drop it into the woven basket"
• —"Move the object from the table into the basket"

Training

Evaluation: Single vs Multi-Instruction Comparison

Evaluated in RoboTwin 2.0 simulator (demo_clean config), 10 episodes, max_steps=400, action_chunk_exec=50, single fixed eval instruction "move the pill bottle to the pad" (fair comparison).

The multi-instruction model trades some single-instruction performance for the ability to follow varied language commands. For tasks where instruction diversity helps (held-out new instructions), this trade-off may pay off.

Usage

from lerobot.policies.smolvla import SmolVLAPolicy

policy = SmolVLAPolicy.from_pretrained("arrow-hf/smolvla-robotwin-move-pillbottle-pad-50ep-multi")

See LeRobot documentation for inference setup.

Citation

Built on SmolVLA and SmolVLA-RoboTwin pretrained base, fine-tuned on data collected from RoboTwin 2.0.