Aikwed/lingbot-vla-pick-and-place-toy-to-bucket

LingBot-VLA: Pick and Place Toy to Bucket

This checkpoint is a full-parameter fine-tune of LingBot-VLA-4B on the `AlvinAi/pick_and_place_toy_to_bucket` LeRobot dataset.

Task instruction

pick and place toy to bucket

The training pipeline formats it as <bos>pick and place toy to bucket\n before tokenization.

Dataset and robot layout

• —82 episodes, 27,962 frames, 30 FPS
• —Cameras: observation.images.front and observation.images.side
• —State/action dimensions:
• —shoulder_pan.pos
• —shoulder_lift.pos
• —elbow_flex.pos
• —wrist_flex.pos
• —wrist_roll.pos
• —gripper.pos
• —Arm joints are trained as delta actions.
• —The gripper is trained as an absolute action.
• —The action horizon is 50 steps, with episode-tail padding excluded from both normalization statistics and the training loss.

The dataset's gripper values are approximately in the range 0–34. Hardware inference must use the same calibration and units; do not assume a 0–100 gripper range.

Fine-tuning setup

• —Full-parameter fine-tuning of the VLM, vision encoder, and action expert
• —4 epochs
• —Global batch size 256 on 4 GPUs
• —Learning rate 2e-5, constant schedule
• —FP32/TF32 FSDP2 training
• —Mean/std normalization from the accompanying norm_stats.json

Included files

• —Hugging Face model weights and tokenizer/processor configuration
• —lingbotvla_cli.yaml: resolved training configuration
• —norm_stats.json: task-specific normalization statistics
• —robot_config.yaml: feature mapping used during training and inference

For inference, use norm_stats.json and robot_config.yaml from this model repository so that input padding, normalization, action restoration, and the absolute gripper output remain aligned with training.