RI - 16662: Robot Autonomy 2018

Instructors: 
Katharina Muelling (kmuelling -- nrec.ri.cmu.edu)

Lectures: 
TBA

Description: 
Robot autonomy delves into the interplay between perception, manipulation, navigation, planning, and learning required to develop fully autonomous systems. We will focus on application domains like the home, retail, and healthcare and identify common themes and key bottlenecks. We will discuss the state of the art algorithms, their computational and hardware requirements, and their limitations. To enable you to create end-to-end systems, you will learn how to address clutter and uncertainty in manipulation tasks, develop robust object recognition algorithms in real-world scenes, plan robot trajectories in high-dimensional spaces, build behavior engines for high-level tasks, and learn to apply and connect those to create an autonomous robot system. The course emphasizes the implementation of the algorithms discussed in class in simulation through homework assignments as well as on real systems in a class project.

Learning Objectives: 
In this course, you will gain knowledge and experience about perception, manipulation, navigation, planning, learning, and system integration – all the components necessary for developing fully autonomous systems. By the end of this course you should be able to:
  • Understand, implement and apply algorithms for robot localization and tracking
  •  Explain the different possibilities how robots can perceive their environment and how to develop robust vision algorithms.
  • Understand and explain the differences between open and closed loop systems.
  • Explain, implement, and compare algorithms for motion planning for high-dimensional spaces
  • Identify, implement and discuss the different components that make up manipulation
  • Understand the sources of uncertainty in robotic systems and explain how to account for them
  • Describe and model behavioral decision processes in form of states and actions and explain the different ways of solving these problems as well as their limitations.
  • Understand the current applications, concepts, and limitations of learning approaches for robot autonomy.
  •  Work efficiently in a team to solve robotic problems.
Syllabus: Can be found here