Perception, Estimation & Robot Learning

Seeing, localising, learning. Kalman filtering, machine vision, cameras and 3-D perception, SLAM, and imitation and reinforcement learning.

• Kalman Filtering & Model Predictive Control — Estimation and optimisation at the top of the control pyramid. Learn the Kalman filter (predict/update, gain, covariance, Q and R tuning), sensor fusion and the extended Kalman filter, and model predictive control (prediction horizon, cost function, constraints, receding horizon, offset-free MPC) — with a running Kalman filter and a constrained MPC in plain Python.
• Machine Vision for Automation — Giving machines eyes. Learn vision-system components (cameras, sensors, lenses, lighting), resolution and field of view, the pinhole camera model and calibration (intrinsics, extrinsics, distortion), image processing (thresholding, filtering, edges, morphology, blobs, template matching), measurement and inspection, 2-D/3-D vision (stereo, structured light, time-of-flight), deep-learning inspection, and vision-guided robotics — with Python for FOV/resolution, blob centroids and pixel-to-world mapping.
• Robot Perception, Localisation & Learning — How robots see, know where they are and learn. Learn robot sensors (cameras, depth cameras, lidar, IMU, force/torque, tactile), the pinhole camera model and intrinsics/extrinsics, camera calibration, stereo and depth, point clouds and pose estimation (ICP, 6-D pose), object detection and grasp detection with deep learning, visual servoing, localisation and SLAM (odometry, particle filters, EKF, graph SLAM), sensor fusion, and robot learning — imitation learning, reinforcement learning (Q-learning, policy gradients), sim-to-real and robot foundation models — with Python projecting a 3-D point to pixels and training Q-learning on a simple task.