Robotics: Algorithms and Applications

MW 3:00-4:20, ARC G070

Instructor: Tapomayukh Bhattacharjee
OH: Thursday 3-4pm, CSE2 277

TA: Brian Hou
TA: Aditya Vamsikrishna Mandalika
OH: Monday 2-3pm, CSE2 152; Wednesday 2-3pm, CSE2 276



Prerequisites: familiarity with mathematical proofs, probability, linear algebra, programming
Grading: Homeworks (60%), Project (30%), Class participation (10%)
Textbooks (optional):

  • Probabilistic Robotics, S. Thrun, W. Burgard, and D. Fox. MIT Press, Cambridge, MA, 2005.
  • Planning Algorithms, Steven M. LaValle. Cambridge University Press.
  • Artificial Intelligence: A Modern Approach (Third Edition), Russell, Stuart J., and Peter Norvig. Pearson Education Limited, 2016.
  • Automated Planning: Theory and Practice, Malik Ghallab, Dana Nau, Paolo Traverso. Elsevier, 2004.
  • Feedback Systems: An Introduction for Scientists and Engineers, Aström, K. J., & Murray, R. M. Princeton University Press, 2018.
  • Reinforcement Learning: An Introduction, R.S. Sutton and A.G. Barto. MIT Press, Cambridge, MA, 2018.
  • Assignments

    Homework assignments will have both written and programming components. Discussion is encouraged, but each student must independently write up and implement their solutions. All collaborators must be listed on each submission.

    Assignments are due online by 11:59 PM on the listed date. Each student has four free late days for the two assignments; if an assignment is submitted after exhausting the late days, it is subject to a 10-point penalty for each day over the budget.

    Writeups must be submitted as a PDF via Gradescope. LaTeX is preferred, but other typesetting methods are acceptable. Code for the programming component must be submitted in a zip archive via Canvas.


    Research projects will be completed in teams of three. Students are encouraged to explore any ideas related to robotics or from their own research. Project deliverables and suggested project ideas are now available here.

    Project proposals, progress reports, posters, and final reports must be submitted via Gradescope.

    Final Projects


    Date Topic Slides, Notes Reading
    Jan 7 Introduction and Bayesian Filtering Slides, Notes PR Ch. 2
    Jan 9 Kalman Filtering Slides, Notes PR Ch. 3
    Jan 14 Particle Filtering Notes PR Ch. 4
    Jan 16 Mapping Slides, Notes PR Ch. 9
    Jan 21 No Lecture --- ---
    Jan 23 Simultaneous Localization and Mapping Slides PR Ch. 10, Ch. 13
    Jan 28 Planning Intro and Search Algorithms Slides, Notes PA Ch. 4, A*
    Jan 30 Sampling-Based Motion Planning Slides PA Ch. 5, RRT
    Feb 4 No Lecture (Snow) --- ---
    Feb 6 Trajectory Optimization Anca Dragan's Intro Notes, CHOMP Notes CHOMP, TrajOpt
    Feb 11 Task and Motion Planning Slides, Slides 2, Lecture Video AI Ch. 10, AP Ch. 2, 4
    Feb 13 Feedback Control Overview Notes FS Ch. 3, Ch. 7-9, Ch. 11
    Feb 18 No Lecture --- ---
    Feb 20 Markov Decision Processes (Debadeepta Dey) Notes, Slides RL Ch. 1, Ch. 2, Ch. 3
    Feb 25 Model-Based RL + Linear Quadratic Regulator and Extensions (Debadeepta Dey) Policy and Value Iteration Notes, LQR Notes LQR, RL Ch. 4
    Feb 27 Model-Based RL + Linear Quadratic Regulator and Extensions (Debadeepta Dey) LQR Tracking Notes, Slides LQR, RL Ch. 4
    Mar 4 Model-Free RL (Debadeepta Dey) ADP Notes, Policy Gradient Notes RL Ch. 13
    Mar 6 Special Topics: Human-Robot Interaction (Maya Cakmak) Slides ---
    Mar 11 Special Topics: Medical Robotics (Blake Hannaford) Slides ---
    Mar 13 Special Topics: Manipulation and Open Problems (Nathan Ratliff) ---