Purdue CS 59300 Robotics (Spring 2022)

This course covers topics in motion planning, estimation, and control to design algorithms for robots to safely interact with their environments and perform various challenging tasks under constraints. The first part of this course focuses on classical techniques such as search-based and sampling-based planning, PID control, Model Predictive Controller (MPC), and Bayesian filters. The second part covers modern deep learning and deep reinforcement learning techniques and their application to planning and decision-making in robotics.

Administrative Information

  • Instructor: Ahmed H. Qureshi (ahqureshi@purdue.edu) | Office Hours & Location: Monday 3:00 - 4:00 pm at HAAS 152

  • TA: Troy William Honegger (thonegge@purdue.edu) | Office Hours: Tuesday 12:00 - 1:00pm or by appointment

  • Time & Location: TTH 9:00-10:15, LWSN 1106

Course Content

Sr Date Topics (Tentative) Readings
1 1/11 Introduction to Robotics
2 1/13 Robot Manipulators (Forward/Inverse Kinematics)
3 1/18 Motion Planning I: Sampling-based methods I (RRT, PRM)
4 1/20 Motion Planning II: Sampling-based methods II (RRTConnect, RRT*, B-RRT*)
1/21 Assignment 1 (Release)
5 1/25 Motion Planning III: Advance Constraints (Kinodynamic, Manifolds, etc.)
6 1/27 Optimal Control I: PID I
7 2/1 Optimal Control II: PID II
8 2/3 Optimal Control III: MPC I
2/4 Assignment 1 (Due 11:59pm (EDT))
2/7 Assignment 2 (Release)
9 2/8 Optimal Control IV: MPC II
10 2/10 Estimation I: Probability Review, Bayes Filtering
11 2/15 Estimation II: Kalman Filter
12 2/17 Combining Planning, Estimation & Control
13 2/22 Function Approximation I: Neural Networks, Backpropagation, Dropout
2/22 Assignment 2 (Due 11:59pm (EDT))
14 2/24 Function Approximation II: Convolutional Neural Networks, Recurrent Neural Networks (LSTMs)
2/25 Assignment 3 (Release)
15 3/1 Function Approximation III: Variational Autoencoders (VAEs), Generative Adversarial Networks
16 3/3 Learning for Planning :Informed Sampling, Path Generation
17 3/8 Deep Reinforcement Learning I: MDPs, Value Function, Q-Function
18 3/10 Deep Reinforcement Learning II: Policy Gradients (Reinforce)
3/11 Assignment 3 (Due 11:59pm (EDT))
19 3/15 Spring Break
20 3/17 Spring Break
3/18 Assignment 4 (Release)
3/18 Project Proposals (Due 11:59pm (EDT))
21 3/22 Deep Reinforcement Learning III: Advance Policy Gradients Methods I
22 3/24 Deep Reinforcement Learning IV: Advance Policy Gradients Methods II
23 3/29 Imitation Learning
24 3/31 Inverse Reinforcement Learning I: Maximum Entropy(MaxEnt)-IRL
4/1 Assignment 4 (Due 11:59pm (EDT))
25 4/5 Inverse Reinforcement Learning II: GANs & IRL
26 4/7 Deep Reinforcement Learning for Planning
27 4/12 Closing Remarks, Discussions & Open Research Problems
4/13 Project Presentation PDF files (Due 11:59pm (EDT))
28 4/14 Project Presentations I
29 4/19 Project Presentations II
30 4/21 Project Presentations III
31 4/26 Project Presentations IV
32 4/28 Project Presentations V
5/3 Project Report, Demonstration Videos, Codes, Trained Models & Datasets(Due 11:59pm (EDT))

Prerequisites

Familiarity with Data Structures & Algorithms, and a background in AI & Statistical Estimation is required. The interested undergraduate students should meet the following prerequisites:

  • CS 25100 Data Structures & Algorithms

  • CS47100-AI 2021 Artificial Intelligence OR CS37300 Data Mining & Machine Learning

Textbooks

There are no specific textbooks. However, some helpful reference books include:

  • Principles of Robot Motion (Theory, Algorithms, and Implementations) by Howie Choset, Kevin M. Lynch, Seth Hutchinson, George A. Kantor, Wolfram Burgard, Lydia E. Kavraki and Sebastian Thrun.

  • Planning Algorithms by Steven M LaValle. Cambridge university press, 2006.

  • Probabilistic Robotics by Wolfram Burgard, Dieter Fox, and Sebastian Thrun. MIT Press (2005).

  • Reinforcement learning: An introduction by Richard S. Sutton, and Andrew G. Barto. MIT press, 2018.

  • Deep Learning by Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. MIT press, 2016.

Grading Policy

There are no midterm and final exams.

Assignments (4 X 10%) 40%
Project proposal 10%
Project Completion & Demo 30%
Project Report & Presentation 15%
Class Participation 5%

Project: Students are encouraged to work as teams of two or three.

Project report template:https://www.overleaf.com/latex/templates/ieee-journal-paper-template/jbbbdkztwxrd

Late Submission Policy: All deadlines are firm unless notified in advance. Late submissions can only be accepted within next 48hr (1second==48hr) of the deadline but will result in a straight 25% off.

Academic Integrity

This course defaults to Purdue standards on intellectual integrity and academic conduct. Therefore, students are responsible for reading the following pages and comply with them throughout this course.