SmolVLA RoboTwin place_object_basket (50 ep, MULTI instruction)
SmolVLA policy fine-tuned on 50 demonstration episodes of the place_object_basket task from RoboTwin 2.0 (demo_clean config), built on the SmolVLA-RoboTwin pretrained base (lerobot/smolvla_robotwin).
See also the single-instruction counterpart: arrow-hf/smolvla-robotwin-place-object-basket-50ep
Task & Training
- Robot: Agilex dual-arm, end-effector control (16D state, 16D action)
- Cameras: 3 RGB streams (240×320, D435)
- Instruction mode: per-episode random instruction from RoboTwin's 100 variations (seed=42)
- Training: bs=32, 6000 steps (~10-25 epochs), AdamW lr=1e-4, cosine warmup=300/decay=6000
- Chunk size: 50
Evaluation
RoboTwin 2.0 sim (demo_clean), 10 episodes, max_steps=400, action_chunk_exec=50, eval instruction "place the object in the basket".
Success rate: 5/10 (50%)
Surprising finding: This is the only task in our 8-task benchmark where multi-instruction training outperforms single-instruction (50% vs 30%, +20pp). Likely because the task involves multiple distractor objects, so the multi version is forced to learn instruction-grounded visual recognition, while single trained on the generic "place the object in the basket" cannot ground.
Usage
from lerobot.policies.smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("arrow-hf/smolvla-robotwin-place-object-basket-50ep-multi")
At inference, use action_chunk_exec=50 (full chunk).
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