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mimiminsoo/
starvla-qwenpiv3-so101-marker

Name: starvla-qwenpiv3-so101-marker
Availability: InStock

lerobot

robotics

vision-language-action

starvla

so101

apache-2.0

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StarVLA-π (QwenPI_v3) — SO-101 marker-in-cup

Qwen3-VL-4B backbone (frozen) + layer-wise cross-DiT flow-matching action head, fine-tuned on new_embodiment (97 episodes / 31291 frames, LeRobot v2.1, so101_follower).

Task: Pick up the marker and place it into the cup, then reset the arm to rest state.
Checkpoint: checkpoints/steps_20000_pytorch_model.pt (20000 steps)
Action space: 6-D absolute joint positions — single_arm (5) + gripper (1)
Cameras: front, wrist — in that order (see below)
Action horizon: 16

Serving

Needs the starVLA repo including examples/SO101_Marker/train_files/data_registry/data_config.py — the server resolves robot_type: so101_marker through the auto-discovered registry, and fails at startup without it. data_config.py is mirrored in this repo for reference.

bash

huggingface-cli download mimiminsoo/starvla-qwenpiv3-so101-marker --local-dir ./so101_marker_ckpt

python deployment/model_server/server_policy_gr00t_zmq.py \
    --ckpt_path ./so101_marker_ckpt/checkpoints/steps_20000_pytorch_model.pt \
    --port 5555 --use_bf16

framework.qwenvl.base_vlm in config.yaml points at Qwen/Qwen3-VL-4B-Instruct, so the base VLM downloads on first run. Inference needs roughly 11GB of VRAM in bf16.

Isaac Sim / LeIsaac client

Speaks the GR00T N1.6 ZMQ protocol, so the LeIsaac client connects unchanged:

bash

python scripts/evaluation/policy_inference.py \
    --task LeIsaac-SO101-CupStack-v0 \
    --policy_type gr00tn1.6 --policy_host localhost --policy_port 5555

Camera order matters. Training fed front then wrist, while LeIsaac's scene declares wrist before front, so the raw wire dict order is reversed. The server orders images by the DataConfig's video_keys, which fixes this — but only on a starVLA checkout that has that ordering fix in deployment/model_server/gr00t_obs_adapter.py. On an older checkout the two views arrive swapped and the policy misbehaves without erroring.

Training

Real SO-101 teleoperation data, not simulation. Evaluating in Isaac Sim is a real-to-sim transfer and success there is not implied by the training loss.