Instructor: Ashutosh Saxena.
Tue, Thu: 1:30-2:30pm.
Class webpage: http://www.cs.cornell.edu/~asaxena/cs6756/
Discussion, announcements: https://piazza.com/class#fall2013/cs6756.
The format of the course will be short lectures and paper presentations, followed by discussion and case studies on the applications.
This course focuses on the recent developments in learning algorithms, with applications to perception and planning tasks. Currently, most works in robotics take a piecemeal approach, where they address these two tasks in isolation. In this course, we will study how to take a unified approach to perception and planning. Specifically, we will study: (a) figuring out the right representation (or learning one), (b) how to handle data, or in other words, convert perception and planning problems into data-driven problems, (c) what should be the goal or criterion of learning (e.g., for planning, just a geometric objective may not be relevant), and (d) how to learn, i.e., useful machine learning algorithms.
Our motivating applications will be 3D Perception and mobile manipulation, but the ideas and machine learning techniques discussed in the course should be applicable to more general AI agents that need to perceive and act.
In each of the following (tentative) course topics, we will start from some background material, and quickly move on to discussing recent papers in respective fields:
Paper reading list: Please join Piazza for the updated list and discussion.
CS 6756 satisfies the breadth, applied, and project requirement for CS PhD students, and can also be used for minor for non-CS PhD students.