VLA Vision-Language-Action Model

VLA (Vision-Language-Action) models are end-to-end policy models that combine visual perception, natural-language instructions, and robot action control. Given camera images, robot proprioceptive state, and a natural-language task description, the model directly outputs end-effector and gripper control actions for imitation learning and real-robot deployment.

This section is based on the OpenPI framework and describes the full workflow from data collection, dataset conversion, and model training to real-robot inference.

Applicable Scenarios

• Fine-tune PI0 / PI05 models using LeRobot-format datasets
• Convert LeRobot v3.0 datasets exported by Data-Processing-Tool to the v2.1 format required by OpenPI training
• Deploy trained checkpoints on robots such as Marvin Pro and execute language-instruction tasks through a WebSocket policy service

End-to-End Flow

Raw acquisition data (BAG / MCAP)
  -> Data-Processing-Tool exports LeRobot v3.0 dataset
  -> convert_v3_to_v2.py converts to v2.1 format
  -> train_pytorch.py trains PI0 / PI05 model
  -> serve_policy_kmd_joint.py starts the policy server
  -> vlahost collects robot state and camera images
  -> openpi_client_policy_http_kmd_joint.py performs inference and sends actions

Observation and Action Format

The current KMD real-robot deployment uses three cameras and a 16D joint-space state:

Field	Type	Description
state	float32 vector	16D state: 7 left-arm joints, left gripper, 7 right-arm joints, right gripper
cam_high	HWC uint8	Main-view camera image
cam_left_wrist	HWC uint8	Left-wrist camera image
cam_right_wrist	HWC uint8	Right-wrist camera image
prompt	string	Natural-language task instruction

Policy output:

Field	Type	Description
actions	[T, 16]	Joint-action chunk in the same order as the 16D state

The model uses degrees internally. Robot-side joint_states.positions normally uses radians. The OpenPI client converts rad -> deg before inference and deg -> rad before sending an action.

Environment Requirements

• Python 3.10+
• uv package manager
• Run all commands from the OpenPI project root
• NVIDIA GPU is recommended for training; inference can run through a policy service on an independent GPU server

Quick Navigation

Document	Content
Dataset Conversion	LeRobot v3.0 to v2.1 format conversion
Dataset Sample	Four-camera video and directory structure description
Environment and Project Setup	OpenPI KMD, GPU, Docker, and robot-side vlahost environment
Model Training	PI0 / PI05 fine-tuning and checkpoint management
Real-Robot Deployment	Policy server and robot client integration