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VLA Client Skill: An End-to-End Solution for Direct Control of Robotic Arms by Large Language Models

A ROS2 skill package enabling visual-language-action closed-loop control, allowing LLMs to directly command robotic arms for complex operations while bypassing latency issues from traditional motion planning.

VLA视觉语言动作机器人控制ROS2机械臂端到端学习大语言模型Robonix
Published 2026-06-14 11:44Recent activity 2026-06-14 11:50Estimated read 5 min
VLA Client Skill: An End-to-End Solution for Direct Control of Robotic Arms by Large Language Models
1

Section 01

VLA Client Skill: Guide to the End-to-End Solution for Direct Robotic Arm Control by LLMs

Project Core

vla_client_rbnx is a ROS2 skill package that implements visual-language-action (VLA) closed-loop control, allowing LLMs to directly call robotic arms to perform complex operations and bypass latency issues from traditional motion planning.

Project Source

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Section 02

Project Background: Paradigm Shift in Robot Control

Traditional robot operation uses a separated perception-planning-execution architecture. For example, a grasping task requires YOLO recognition → geometric calculation → MoveIt planning, with each step taking 3-5 seconds, which cumulatively affects the efficiency of complex tasks.

The rise of VLA models brings an end-to-end control paradigm: directly generating action sequences from visual inputs and natural language instructions without intermediate representations, making it possible for LLMs to call robot skills.

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Section 03

System Architecture and Core Methods

Closed-Loop Control Pipeline

Data flow: Camera topic → observe → VLA server → safety filter → /arm/pos_cmd → piper_ctl → robotic arm (joint state feedback closed loop)

Key Design

  • Bypass MoveIt: Directly send commands to piper_ctl to achieve a 10Hz control frequency
  • Multi-source vision: Global camera (scene understanding) + wrist camera (fine operation), uniformly resized to 256x256
  • Body perception: Joint states (/arm/joint_states_single) + end pose (/arm/end_pose)
  • Service discovery: Default Atlas dynamically discovers VLA servers, supports direct connection debugging
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Section 04

Safety Mechanisms: Guarantees After Bypassing MoveIt

Built-in safety filters:

  1. Joint limits: 6-joint angle limited to ±2.618 radians (±150 degrees)
  2. Rate limit: Single-step joint change ≤0.1 radians
  3. Gripper range: Opening/closing value between 0.0-1.0
  4. Emergency reset: manipulation/reset service calls MoveIt to return to the parking position
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Section 05

Comparison and Effect Evidence

Feature Traditional pick_skill_rbnx VLA solution vla_client_rbnx
Method YOLO+Geometry+MoveIt End-to-end VLA model
Control frequency 3-5 seconds/step 10Hz (100ms/step)
Safety mechanism MoveIt collision detection Built-in filter
Applicable scenarios Simple grasping Complex language-guided operations
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Section 06

LLM Call Interfaces and Deployment Dependencies

LLM Interfaces

  • robonix/skill/vla/driver: Skill lifecycle management
  • robonix/skill/vla/execute: Input natural language instructions (e.g., "Put the red block into the blue box") to execute automatically

Deployment Dependencies

  1. ROS2 Humble + rclpy
  2. vla_server_rbnx (GPU inference)
  3. OrbbecSDK_rbnx (camera stream)
  4. piper_ctl_rbnx (execute commands)
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Section 07

Technical Significance and Future Outlook

Significance

  • Paradigm shift: From layered architecture to end-to-end learning,打通ing the channel from natural language to physical actions
  • Developers: No need for complex kinematics code
  • Users: Interact with robots using daily language

Challenges

Need to solve VLA model generalization, safety, and hardware migration issues; it is an important step towards general-purpose robot assistants.

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