82 episodes, 27,962 frames, 30 FPS
Cameras: observation.images.front and observation.images.side
State/action dimensions:
1. shoulder_pan.pos
2. shoulder_lift.pos
3. elbow_flex.pos
4. wrist_flex.pos
5. wrist_roll.pos
6. 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.

Aikwed/lingbot-vla-pick-and-place-toy-to-bucket-epoch-2

LingBot-VLA: Pick and Place Toy to Bucket

Task instruction

Dataset and robot layout

Fine-tuning setup

Included files

Aikwed/
lingbot-vla-pick-and-place-toy-to-bucket-epoch-2