CS296-3 Robot Learning and Autonomy

Instructor: Prof. Chad Jenkins
W 2:30-5:20


This course attempts to address the question "What are the driving applications of robotics?" How will robots move out of structured laboratory settings into real-world applications where a diversity of users, environments, and tasks abound. Towards this end, CS296-3 is a seminar course that covers current research topics related to perceiving and acting in the real world. These topics will be pursued through independent reading, class discussion, and project implementations. Papers covered will be drawn from robotics, computer vision, animation, machine learning, and neuroscience. Special emphasis will be given to developing autonomous control from human demonstration and video game style interfaces.


Grading for individual enrolled students is broken down as follows:

20% Attendance and participation
40% Paper presentations
40% Contribution towards final project

Students are expected to attend all class meetings (unless an exception is given beforehand), actively participate in discussion, present 2 papers to the class, and significantly contribute towards the development and implemenation of a final project.

For paper presentations, student presenters must have a rough draft prepared and consult with the instructor at least 2 days before the presentation date.

Tentative schedule

Each class meeting will consist of 2 paper presentations given by students. This should take between 1-2 hours. The remaining time will be devoted to a collaborative hacking session to implement and try-out new ideas.

1/24 Introduction

Discussion: "What are the driving applications of robotics?"
iRobot Create and Player/Stage demo

1/31 Brainstorming

Discussion: "Project Ideas"
Player/Stage Introduction
SDL Introduction
Roomba/Create Open Interface

2/7 Papers Fast Forward

2/14 Autonomous control architectures

2/21 Motor learning and neuroscientific models

2/28 Robot learning: nonparametric regression

3/7 Interfaces for Robot Teleoperation

3/14 Robot learning: reinforcement learning

3/21 Manifold learning and dynamical systems

3/28 Spring Break

4/4 Image features and object recognition

4/11 Social robotics

4/18 Group behavior and task allocation

4/25 Manipulation

5/2: Open for paper selections

5/9: Final project demos