Syllabus

Introduction to the science and design of robots - behavior-based embodied artificial intelligence, kinematics and inverse kinematics, geometric reasoning, motion planning, mapping, and manipulation, dynamics, biologically inspired and biomimetic robotics, distributed robotics and intelligence, and some philosophical questions pertaining to the nature of intelligence in the physical world.

Introduction to Robotics, robotics Motion Control, Probability/Sensing, Basics of Kalman Filters and the invariants, Localization (SLAM,V-SLAM)

Computer Vision for Robotics, Robotic vision sensors and their interfacing, Action and Sensor Modeling

Behavior Architectures, Planning with Markov Decision Processes (MDPs) Partially Observable Markov Decision Processes (POMDPs), Motion Planning, Sampling Methods in Motion Planning for robotics, Path Planning algorithms for Robots, introduction to Reinforcement Learning for robotic application, Industrial Applications and Social aspects of robotics