This course is intended to serve as an advanced overview of robotics, with an emphasis on perception and planning. We will study algorithms and data structures related to these topics, covering widely adopted, and state of the art techniques. Students will gain hands-on experience in implementing, and extending such algorithms using real robot data, as well as simulations.

After successfully taking this class, you will be able to implement:

  1. A particle filter for mobile robot localization
  2. A pose-graph SLAM backend solver for a ground robot using vision and odometry
  3. An RRT planner for kino-dynamic path planning
  4. A performant state of the art A* solver with Jump-Point Search

Suggested text book: Probabilistic Robotics

  • Gradescope:
  • Piazza: There is no Piazza for this class, and this is by design. If stuck, follow these steps in order:
    1. Re-read your lecture notes, the assigned reading material, and the class lecture slides on the topic.
    2. Stop, and medidate on the problem.
    3. If still stuck, formulate exactly what you do not understand, and ask it during office hours.
  • Assignment PDFs and LaTeX templates

Gradescope sign-up instructions:

  1. Log in to gradescope using your UMass email address.
  2. Click on “Add a course”, and enter code MNPW4Z.
  3. Once signed up, course access link above will work.

Lectures, Office Hours

Lectures: TuTh, 1:00PM - 2:15PM, LGRC A311

Joydeep Biswas,
Office hours: Fridays, 2:30-3:30PM, LGRC A325

Teaching Assistant
Spencer Lane,
Office hours: Tuesdays 2:15 - 3:30, and Wednesdays 10:30 - 11:30, LGRT T220