Have you ever driven a car? If not, have you ever sat in a car? Tried to avoid getting hit by a car while walking across the street? Tried to design a car?

This course will explore some of the issues around this classic and familiar example. (although this material and the underlying theory can be applied to many domains, including medical devices, aerospace engineering, biologicial systems, and others)

It is not your typical course on automatic controls. While we will achieve a high level of rigour in the theory and analysis of classical and modern feedback control, we will focus on design issues centered around the interaction between humans and autonomy and/or the interactions between autonomous systems and other autonomous systems. The development of, for instance, linear quadratic regulation (or estimation) will be grounded in familiar case studies.

Intended Learning Objectives

  1. Design planners and controllers using state-space, search, and optimization methods

    1. Indicate the robustness of design

    2. Indicated generality of algorithms

  2. Design automated planning and perception systems

    1. Centralized planning

    2. Decentralized planning

    3. Sharing of information

  3. Understand impact of implementation issues

    1. nonlinearity, delay, noise

    2. human usage and interactions

    3. interactions with physical systems and other autonomy

Expectations and Policies

  • Graduate students will do all the work of the undergraduates,

    • plus additional problems on homework sets and

    • short class project due at the end of the semester

  • Honor code; some assignments may be collaborative


Class Date Topic Lecture Notes
1 Jan 17 Controls & Introduction
2 Jan 22 Automation Feedback and Control HW1 Out
3 Jan 24 Dynamic Systems
4 Jan 29 Stability and Performance
5 Jan 31 Optimal Control HW1 In
6 Feb 5 Motion Planning HW2 Out
7 Feb 7 Motion Planning
8 Feb 12 Workshop Day
9 Feb 14 Perception HW2 in
10 Feb 19 Inference and Prediction HW3 Out
11 Feb 21 Inference and Prediction
12 Feb 26 Game Theory
13 Feb 28 Workshop Day HW3 In
Mar 5 Spring Recess
Mar 7
14 Mar 12 Collaborative Systems Motivating Examples Presentations
15 Mar 14 Robot control architectures Student-led
16 Mar 19 discussions
17 Mar 21 Distributed consensus
18 Mar 26
19 Mar 28 Self-organization, formations
20 Apr 2
21 Apr 4 Coordinated Systems
22 Apr 9
23 Apr 11 Human Supervisory Control
24 Apr 16
25 Apr 18 Learning in Collaborative Systems
26 Apr 23
27 Apr 25 Projects
28 Apr 30 Projects