TommyZihao/vlm_arm

What it solves

This project provides a framework for integrating large multimodal models (LMMs) with physical robotic arms, enabling them to understand natural language commands and visual input to perform complex tasks.

How it works

The system acts as an intelligent agent that bridges the gap between high-level AI reasoning and low-level hardware control. It uses a Vision-Language Model (VLM) to process visual data and user instructions, identifies the target coordinates, sequences the necessary physical movements, and formats the output to control the robotic arm (specifically the Elephant Robotics MyCobot 280 Pi).

Who it’s for

It is designed for developers, researchers, and hobbyists interested in embodied AI and robotics who want to experiment with connecting state-of-the-art AI models like GPT-4o, Yi-Vision, or Qwen-VL to physical hardware.

Highlights

• Supports multiple high-end multimodal models including GPT-4o, Claude 3 Opus, and Qwen-VL-Max.
• Provides a clear pipeline for translating visual and linguistic cues into robotic motion coordinates.
• Includes documentation for hardware integration with the MyCobot 280 Pi and Raspberry Pi 4B.
• Demonstrates real-world applications like object manipulation and interactive robotic tasks.