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arrow-hf/smolvla-robotwin-place-container-plate-50ep-multi

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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SmolVLA RoboTwin `place_container_plate` (50 ep, MULTI-instruction)

SmolVLA policy fine-tuned on 50 demonstration episodes of the `place_container_plate` 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-place-container-plate-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

Config	Value
Base checkpoint	`lerobot/smolvla_robotwin`
Training data	50 RoboTwin demonstrations, 50 unique instructions
Batch size	32
Steps	6000 (~10-25 epochs)
Optimizer	AdamW, lr=1e-4
Scheduler	Cosine, warmup=300, decay=6000
Chunk size	50

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 "place the container on the plate" (fair comparison).

Variant	Eval setting	Success rate
Single-instruction training	Fixed `"place the container on the plate"`	9/10 (90%)
Multi-instruction training (this model)	Fixed `"place the container on the plate"`	7/10 (70%)

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

python

from lerobot.policies.smolvla import SmolVLAPolicy

policy = SmolVLAPolicy.from_pretrained("arrow-hf/smolvla-robotwin-place-container-plate-50ep-multi")

See LeRobot documentation for inference setup.

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