CS287 Home Page

University of California at Berkeley
Dept of Electrical Engineering & Computer Sciences

CS 287: Advanced Robotics, Fall 2011

Fall 2009 offering (not particularly closely matched to current year's offering)

Instructor: Pieter Abbeel
Lectures: Tuesdays and Thursdays, 3:30pm-5:00pm, 320 Soda Hall
Office Hours: Wednesdays 2:00-3:00pm (and by email arrangement) in 746 Sutardja Dai Hall
Communication: Piazza is intended for general questions about the course, clarifications about assignments, student questions to each other, discussions about material, and so on. I strongly encourage all students to participate in discussion, ask, and answer questions through this site. I will monitor discussions closely. To sign up, go to the Piazza website and sign up with "UC Berkeley" and "CS287" for your school and class.

Announcements
Assignments
Prerequisites
Class goals
Grading
Assignment policy
Syllabus and materials
Related materials
CS 287 Fall 2011 Roster of Students

Announcements

Please sign up on Piazza for CS287 Advanced Robotics for all future announcements.
Welcome to the Fall 2011 edition of CS287!

Assignments

All assignments and data provided in assignments is licensed under a Creative Commons Attribution 3.0 Unported License. Attribution: "This work was authored by Pieter Abbeel for the Fall 2011 offering of CS 287 Advanced Robotics at UC Berkeley."

Prerequisites

Familiarity with mathematical proofs, probability, algorithms, linear algebra; ability to implement algorithmic ideas in code.
Consent of instructor required for undergraduate students. Come see instructor after lecture or during office hours.

Class Goals

Master the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to strengthen your expertise in these two areas.
Implement and experiment with these algorithms.
Be able to understand research papers in the field of robotics
- Main conferences: ICRA, IROS, RSS, ISER, ISRR
- Main journals: IJRR, T-RO, Autonomous Robots
Try out some ideas/extensions of your own
Note: the focus of the course is on math and algorithms. We will not study mechanical or electrical design of robots.

Grading

Open-ended final project (40%)
Assignments (60%)

Assignment policy

Collaboration: Students may discuss assignments. However, each student must code up their solutions independently and write down their answers independently.
Late assignments: Recognizing that students may face unusual circumstances and require some flexibility in the course of the semester, each student will have a total of seven free late (calendar) days to use as s/he sees fit. Late days are counted at the granularity of days: e.g., 3 hours late is one late day. If an assignment is submitted beyond the late-day budget, you will lose 20 (out of 100) points per day over budget (but you cannot go below zero).
Late days cannot be used for the final project.
Final project guidelines

Syllabus and materials

A tentative list of topics includes (subject to change!):

Estimation (6 weeks): Bayes filters, KF, EKF, UKF, particle filter, occupancy grid mapping, EKF slam, GraphSLAM, SEIF, FastSLAM
Optimal Control (3 weeks): Globally Optimal Control through Discretization, Locally Optimal Control through Sequential Quadratic Programming, MPC, LQR, LQG, iterative versions
Motion Planning (1 week): RRT (+variants), A* (+variants)
RGB-D and Point Clouds (2 weeks): features, Ransac, Hough, instance retrieval/detection
Manipulation and Grasping (1/2 week): grasp quality metrics, grasp strategies, caging
Reinforcement Learning (1 week): policy gradient
Case Studies (2 weeks): autonomous helicopter, autonomous driving, walking, mobile manipulation.

PR = Probabilistic Robotics, Thrun, Burgard, Fox, MIT Press 2006.
All slides are available here.
A couple of students have volunteered to record and post lecture videos. They are posting them here.
If your probability is rusty, you might want to handpick some homework/section exercises from past CS188 offerings, located here and similar url's replacing sp11 with fa10, sp10, fa09, etc.

Lecture	Topic	Readings	Optional/Additional Readings
Th Aug 25	Course Introduction
Tu Aug 30	Probability Review, Bayes Filters	Intro: PR 1; Probability Review and Bayes Filters: PR 2
Th Sep 1	Motion Models, Beam Sensor Models, Particle Filters and Localization	Motion Models: PR 5.1, 5.2, 5.3; Beam Sensor: PR 6.1, 6.2, 6.3; Particle Filters: PR 4; Localization: PR 8.1, 8.3
Tu Sep 6	Particle Filters++	Particle Filters: PR 4	Doucet, Godsill, Andrieu, 1998, Arulampalam et al., 2002
Th Sep 8	Mapping with Known Poses	Mapping with Known Poses: PR 9
Tu Sep 13	Mapping with Known Poses, Rao-Blackwellized Particle Filters		Doucet, de Freitas, Murphy, Russell
Th Sep 15	gMapping, Scan Matching	Grisetti, Stachniss, Burgard T-RO 2006
Tu Sep 20	Multivariate Gaussians	PR 3
Th Sep 22	Kalman Filtering	PR 3	From Gauss to Kalman
Tu Sep 27	EKF, UKF	PR 3	Julier and Uhlmann, the UKF
Th Sep 29	Smoother, MAP
Tu Oct 4	Maximum Likelihood, EM
Th Oct 6	SEIF, EnKF, EKF-SLAM, FastSLAM, GraphSLAM (, SE(3))
Tu Oct 11	MDP's, Value Iteration	Sutton and Barto, Reinforcement Learning, Chapters 3 and 4
Th Oct 13	Discretization of Continuous State Space MDP's		Moore and Atkeson, 1993, Munos and Moore, MLJ 2001, Chow and Tsitsiklis, 1991, Gordon, 1995, Tsitsiklis and Van Roy, 1996, Kushner and Dupuis, 1992/2001,
Tu Oct 18	Autonomous Driving --- IROS Lecture by Thrun and Urmson
Th Oct 20	LQR, iterative LQR / Differential Dynamic Programming
Tu Oct 25	No Lecture
Th Oct 27	No Lecture
Tu Nov 1	Unconstrained Nonlinear Optimization, Constrained Nonlinear Optimization, Convex Optimization Problems		Boyd and Vandenberghe, Chapters 9 -11
Th Nov 3	Unconstrained Nonlinear Optimization, Constrained Nonlinear Optimization, Convex Optimization Problems (These are same set of slides as linked to in previous lecture.)	cvx_example.m	Boyd and Vandenberghe, Chapters 9 -11
Tu Nov 8	Locally Optimal Control through Nonlinear Optimization: Shooting Methods, Collocation Methods, Model Predictive Control (MPC); POMDPs	code examples
Th Nov 10	Motion Planning: PRM, RRT + variants	Steven M. Lavalle, Motion Planning, Chapters 5, 14, code example
Tu Nov 15	Motion Planning: A* + variants, Likhachev slides	Steven M. Lavalle, Motion Planning, Chapters 5, 14	Videos: Urban Challenge Parking, Quadruped, Mobile Manipulation; Papers: Likhachev, Gordon, Thrun, ARA: Anytime A with Provable Bounds on Sub-Optimality, van den Berg, Shah, Huang, Goldberg, Anytime Nonparametric A, Koenig, Likhachev, Furcy, Lifelong Planning A
Th Nov 17	Object Instance Retrieval/Detection		To learn (much much) more, take CS280 next semester.
Tu Nov 22	Grasp Quality Metrics, Optimizing Grasp Quality	Murray, Li, Sastry Chapter 5
Th Nov 24	Happy Thanksgiving!
Tu Nov 29	Personal Robotics. Guest Lecture by Steve Cousins, CEO Willow Garage	abstract
Th Dec 1	Case Study: Autonomous Helicopter
Tu Dec 6	Project Presentations 3-6pm, 310 Soda
Th Dec 8	Project Presentations 2-5pm, 310 Soda

Related materials

Most relevant book for first 4 weeks:

Thrun, Burgard, Fox, Probabilistic Robotics

There is no direct match for later lectures, but here are some related resources:

If you want to brush up your linear algebra background, I suggest working through this course (video lectures and homeworks available online) at your own pace: Stephen Boyd's EE263: Introduction to Linear Dynamical Systems. fwiw: Stephen Boyd is my personal favorite instructor.
If you want to learn more about the linear systems aspects (Kalman filtering, LQR), I recommend Stephen Boyd's EE363: Linear Dynamical Systems.
If you want go deeper into the theory of linear systems, I recommend: Claire Tomlin's EE221a: Linear System Theory
If you want to learn more about convex optimization, I recommend: Stephen Boyd's EE364a: Convex Optimization I and Stephen Boyd's EE364b: Convex Optimization II. Both of them have all course materials, including lecture videos, available online.
For (although draft-status) more about optimal control and motion planning, Russ Tedrake's class: Underactuated Robotics: Learning Planning, and Control for Efficient Agile Machines could give you a somewhat different angle, some complementary ideas, and more examples.
A more traditional book on control theory: Astrom and Murray, Feedback Systems
A more traditional book on nonlinear control: Slotine and Li, Applied Nonlinear Control. Fwiw: there also seems to be a pdf circulating when typing title of book into google, unlikely to be legal.
A great introductory text on reinforcement learning: Sutton and Barto, Reinforcement Learning
A more mathematically oriented text on reinforcement learning: Bertsekas and Tsitsiklis, Neuro-dynamic programming
Past offerings of my graduate class had more on reinforcement learning / approximate dynamic programming than the current offering: CS294-40, Fall 2008 and CS287, Fall 2009