Vision-Language-Action Model.

ROBOTICS_AI C++ / PYTORCH EMBODIED_INTELLIGENCE

Fig 1.0: Demonstration of real-time VLA inference on an industrial manipulator.

The Architecture

This system integrates a large-scale Vision-Language model with a real-time robotic action controller. The primary challenge was the GPS-denied environment navigation while maintaining semantic awareness of objects in the scene.

By tokenizing both visual frames and natural language instructions, the transformer outputs high-frequency motor commands (Joint Velocities) directly. This bypasses the need for traditional modular planning stacks, reducing latency by 40%.

Tech Stack

  • • NVIDIA TensorRT
  • • ROS2 Humble
  • • Transformers (HuggingFace)
  • • CUDA Optimization

Key Results

  • • 94% Task Accuracy
  • • Zero-shot adaptation
  • • 15Hz Inference Speed
Robot in lab
Software inference screen

Switch System

Next Module
GPS-Denied SLAM
Alternative System
3D Semantic Mapping