University of California at Berkeley

Dept of Electrical Engineering & Computer Sciences

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

- Announcements
- Assignments
- Prerequisites
- Class goals
- Grading
- Assignment policy
- Syllabus and materials
- Related materials

- Please sign up on Piazza for CS287 Advanced Robotics for all future announcements.
- Welcome to the Fall 2012 edition of CS287!

- Problem Set 3: ps3.pdf, starter code. Due: Monday November 12th at 23:59pm.
- Problem Set 2: ps2.pdf, starter code. Due: Monday October 29th at 23:59pm.
- Problem Set 1 (updated 2012/9/23, 00:16): ps1-v2.pdf, starter code v3, Due: Monday September 24th at 23:59pm.
- Final project guidelines

- 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.

- 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 generally 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.

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

- 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

All slides are made available here as the semester progresses.

In the Fall 2011 edition A couple of students have volunteered to record and post lecture videos. They posted them here. They might be of interest this year, too.

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 sp12 with fa11, sp11, fa10, sp10, fa09, etc.

- Thrun, Burgard, Fox, Probabilistic Robotics

- 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.
- 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.
- 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
- The current (=Fall 2012) offering will have some pretty strong overlap with the Fall 2011 offering.
- Earlier offerings of my graduate class had more emphasis on reinforcement learning / approximate dynamic programming than the current offering: CS294-40, Fall 2008 and CS287, Fall 2009