• Announcements
• Assignments, Midterm, Final Project Schedule
• Prerequisites
• Class goals
• Grading
• Assignment policy
• Syllabus and materials
• Related materials

Announcements

• For all future announcements, please sign up on Piazza for CS287 Advanced Robotics.
• Welcome to the Fall 2019 edition of CS287!

Assignments, Midterm, and Final Project Schedule (subject to change)

• Due: Wed 9/18     (Out: Thu 9/5)     Problem Set 1: Value Iteration, Discretization-based Optimal Control, Function Approximation Optimal Control (hw1.zip)
• Due: Wed 10/2     (Out: Thu 9/19)   Problem Set 2: LQR, iLQR, DDP, Feedback Linearization (hw2.zip)
• Due: Mon 10/21   (Out: Sun 10/6)   Problem Set 3: Convex Optimization, Sequential Convex Programming, Optimization-based Motion Planning and Control (hw3.zip)
• Due: Mon 11/4     (Out: Sat 10/20) Problem Set 4: Multivariate Gaussians, Kalman Filtering, Maximum Likelihood, EM, Particle Filtering, Belief Space Planning (hw4.zip)
• Due: Wed 11/6     Final Project Proposal
• Due: Wed 12/4     (Out: 11/15)   Problem Set 5: Reinforcement Learning (hw5.zip)
• Thursday 11/14   Midterm (study handout)
• Week of 12/9-13   Final Project Presentations
• Due: Sun 12/15     Final Project Reports

Assignment policy

• Collaboration: Students may discuss assignments. However, each student must code up and write up their solutions 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).
• Drop-the-lowest: Students are strongly encouraged to complete all 5 assignments. Our grading policy will drop your lowest scoring assignment.

Final Project

• An algorithmic or theoretical contribution that extends the current state of the art.
• An implementation of a state-of-the-art algorithm using real-world data and/or robots.

Logistics and Timeline

• 1 or 2 students per project.
• November 6th: Project Proposals due: 1 page.
• Week of December 9-13: Project Presentations.
• December 15th: Final Paper due. This should be a 6 page paper, structured like a conference paper. I.e., focus on the problem setting, why it matters and what's interesting/novel about it, your approach, your results, analysis of results, limitations, future directions. Cite and briefly survey prior work as appropriate but don't re-write prior work when not directly relevant to understand your approach.
• Late days cannot be used for the final project.

Prerequisites

• Familiarity with mathematical proofs, probability, algorithms, linear algebra; ability to implement algorithmic ideas in code.
• Key Berkeley courses that can build your background: CS188, EE126, EE127. If your foundations are rusty, we recommend you refreshing them through those courses
• Priority goes to PhD students. Consent of instructor required for undergraduate masters students.

Class Goals

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

Grading

• Assignments (60%)
• There are 5 assignments.
• We'll drop your lowest scoring assignment.
• Your remaining 4 (highest scoring) assignments will each equally contribute 60/4 of the points.
• Open-ended final project (30%)
• 1/5 quality of presentation, 1/5 quality of writing of the final paper, 3/5 quality of the results themselves.
• Midterm (10%)

Syllabus and materials

Tentative schedule (subject to change):

Related materials

• 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
• 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