Introduction to Robot Autonomy

What You’ll Learn

Robot Autonomy delves into the interplay between perception, manipulation, planning, and learning for autonomous systems. These components are required to develop autonomous robots for a wide range of domains including households, manufacturing, service industries, and healthcare. The course will explain the implementations, applications, and limitations of algorithms for each component, as well as how to combine them to create fully autonomous robot systems. The course emphasizes the implementation of the algorithms discussed in class through homework assignments in simulation as well as labs and class projects on real robots.

This introductory course will focus on teaching students core topics of robotics and AI through lectures and hands-on labs. 


Course Topics:

  • Forward kinematics
  • Inverse kinematics
  • Collision checking
Low-level Control
  • PID control
  • Interaction control
  • Model-based control
Motion Planning Fundamentals 
  • Configuration spaces
  • Combinatorial motion planning
  • Discrete search algorithms
Sample-based Motion Planning
  • Rapidly-exploring Random Trees
  • Probabilistic Roadmaps
Planning with Costs and Constraints
  • Planning with kineodynamic constraints
  • Planning with costs
Task Planning
  • Predicate-based task representations
  • Hybrid systems and grasping
  • Representing uncertainty
  • Filtering for state estimation
Robotic Systems
  • System Engineering
  • System Design