David Meger
Office: McConnell 112N
Office Hours: Tu 3-4pm
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Sept 14, 2017 The list of assigned papers for student-lead presentations can be found in this Google Doc. Please sign up exactly once. Each presentations will be 15 minutes plus 5 minutes for questions. You are encouraged to explore the code, demos, videos, data etc that go with many of the papers and show those live during your presentation. Your main goal is to teach the rest of the class about the important parts of the method: what are the key steps in the math, what data structures are used and why, and what are the pros and cons? Everyone should try to read the papers at least briefly before the presentations so you come ready with some good questions.


COMP 765 is a research seminar on Intelligent Robotics: the intersection of rocket science and the latest machine learning on-board systems that can sense the world and act upon it. The class will begin with lectures on definitional problems and algorithms in robotics. We will then transition to mixed student-lead and instructor-lead discussions of recent developments in research and in practice. The emphasis is on algorithms, probabilistic reasoning, learning to improve behaviors using data, and decision making under uncertainty, as opposed to electromechanical systems design.

We will broadly cover the following areas:


(Note: this will be populated as we go with the slides, additional links, and information)
Week Topics Slides References
Sept 5 Introduction
Motivation, logistics, rough description of assignments, sense-plan-act paradigm, introduction to modeling perception and action.
1 - Introduction
2 - Spatial Representation
PR Chapters 1, 5 and 6.
Sept 12 Bayesian Estimation
Recursive Bayesian Filter derivation, Kalman filter, particle filter.
3 - Bayesian Estimation
4 - Particle and Kalman Filters
PR Chapters 7 and 8.
Sept 19 Modern Localization and Mapping
Rao-Blackwellized Particle Filtering, large-scale sparse graph optimizers, mapping an entire city, multi-sensor fusion techniques.
5 - EKF and Mapping
PR Section 3
Sept 26 Control Introduction
Basic analysis of control system, ODE background, stability, controllability, PID controllers and tuning.
Guest Lecture on Control Intro
Daves slides on PID
Oct 3 Optimal Control
Relationship between control and Reinforcement Learning, MDP formulation, dynamic programming, trajectory optimization, LQR and DDP.
6 - Optimal Control
7 - Trajectory Optimization
PR Chapter 14.
Oct 10 Geometric Planning
Planning in 2D and 3D, for simple and complex systems. Solutions to the piano-movers problem Spatial decompositions, probabilistic roadmaps, RRT.
8 - Geometric Planning
Planning Algorithms book by Lavalle
Oct 17 Planning Under Unceratinty
POMDP formulation, information-theoretic planning heuristics.
9 - POMDPs
PR Chapters 16 and 17
Oct 24 Learning for Robotics
Machine Learning for robotics problems. Linear regression, Gaussian Processes. Active learning , black-box optimization applied to probabilistic search.
10 - GPs for Robotics GPforML Chapters 1-3
Iain Murray GP slides
Wolfram Burgard GP slides
Oct 31 Learning to Control
Model-based policy search, PILCO, GPS, PDDP. Adapting behavior to changing models, MPC.
Nov 7 Student-lead Research Paper Presentations
Each student will present one paper they have selected.
Nov 14 Imitation Learning
Paradigms for imitation including LfD, IRL and AfP.
Nov 21 Collaborative and Cloud Robotics
Controlling a multi-robot team, centrallized vs decentralized reasoning, planning and control, communication constraints.
Nov 28 Human-Robot Interaction
Invited talk from HRI expert. HRI for home robots, self-driving and robots in the workplace. Safety assessment of intelligent robots.
Dec 5 Project Demonstrations and Wrap-Up


Assignments will be short implementations of the core algorithms seen in lecture for the first 2 months. Starter code and simulation environments are provided, students must implement only the core algorithmic components and prepare a brief report describing their findings.

Marking scheme

Recommended, but optional, textbooks

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