Skip to main content

Module 3: Sensors & AI Integration

Module Overview​

Integrate perception sensors (LiDAR, cameras, IMU) with cutting-edge AI models, including Vision-Language-Action (VLA) pipelines for embodied intelligence. Learn sensor fusion, calibration, and how foundation models enable zero-shot robot control.

Duration: Weeks 8-9 Prerequisites: Module 1-2, Python machine learning basics Learning Outcomes:

  • Integrate LiDAR, RGB-D cameras, and IMUs with ROS 2
  • Process sensor data with computer vision (OpenCV, PyTorch)
  • Implement VLA pipelines for vision-conditioned robot control
  • Apply sensor fusion techniques for robust state estimation

Lessons​

Week 8: Sensor Integration (LiDAR, Cameras, IMU)​

Connect physical sensors to ROS 2, calibrate extrinsics, fuse data with Kalman filters, and visualize in RViz.

Key Topics: Sensor drivers, coordinate frames, TF2, Kalman filtering, point cloud processing

Week 9: Vision-Language-Action (VLA) Models​

Explore VLA architectures (RT-1, RT-2, Octo) that map language instructions + camera images → robot actions.

Key Topics: Transformer-based VLA, tokenization, action prediction, fine-tuning, zero-shot generalization

Module Resources​

  • Sensors: Velodyne/Ouster LiDAR, RealSense D435i camera, Bosch BNO055 IMU
  • Libraries: OpenCV, PyTorch, Transformers (Hugging Face), RT-1/RT-2 checkpoints
  • Datasets: Open X-Embodiment, RoboNet, BridgeData for VLA training
  • Hardware: NVIDIA GPU (RTX 3060+) for VLA inference

Assessment​

  • Weekly Quizzes: 7 questions each on sensor theory and VLA architectures
  • Hands-On: Build a sensor fusion node, run a pre-trained VLA model
  • Project: Deploy a VLA pipeline on a simulated robot for tabletop manipulation

Next: Start with Week 8: Sensor Integration →