ECE598SG: Special Topics in Learning-based Robotics

Fall 2019


This course will introduce students to recent developments in the area of learning-based robotics. The course will start with the instructor providing an overview of background material from relevant sub-fields: computer vision, machine learning, reinforcement learning, control theory and robotics. This will be followed by discussion of advanced techniques for arriving at policies for robots, such as model learning, model-based RL with learned models, imitation learning, inverse reinforcement learning, self-supervised learning, exploration, and hierarchical reinforcement learning, and application of these concepts to robot navigation and manipulation. This part of the course will be covered via student-led discussion of recent research papers that develop and validate these techniques. Course also includes open-ended project work that will provide students a flavor of how to conduct research in this emerging area.

Quick Info


Learning Outcomes

After taking this course you will be able to:


This is an advanced gradate course aimed at graduate students conducting research in relevant research areas. The course will largely cover relevant papers published within the last few years in computer vision, robotics, and machine learning. Students should be familiar with reading and critiquing research papers, and should have a basic understanding of concepts in artificial intelligence, and machine learning. Students must have taken at least one of the following (or equivalent) courses: ECE 448 / CS 440 (Introduction to Artificial Intelligence), ECE 544NA (Pattern Recognition), ECE 549 / CS 543 (Computer Vision). If you are not sure whether you meet the prerequisites, talk to the instructor after the first class or in office hours.


Here is a tentative syllabus for the course. TBD readings will be filled in over time, but at least 2 weeks before class.

Date Topic Material / Readings
Aug 27 Introduction and Course Overview  
  Part 1: Background  
Aug 29 Computer Vision Review 3D Reconstruction, Recognition, CNNs for Recognition.
See also Szeliski Chapters 4, 7, 14.
Sep 03 Robotics Review Configuration Space, Forward Kinematics, Inverse Kinematics, Motion Planning, Optimal Control.
See also Modern Robotics Chapters 2, 4, 6, 10.
Sep 05 MDP Review Terminology, Policy Evaluation, Policy Improvement, Policy Iteration, Value Iteration. Class slides.
See also: David Silver’s slides here and here, and Sutton and Barto Chapters 3, and 4.
Sep 10 MDP Review Model Free Reinforcement Learning: Monte-Carlo and Temporal Difference Learning and Control, Off-policy learning. Class Slides.
See also: David Silver’s slides here and here, and Sutton and Barto Chapters 5, 6, and 7, Playing Atari with Deep Reinforcement Learning.
Sep 12 MDP Review Model Free Reinforcement Learning: Deep Q-learning, Policy gradients. See also: David Silver’s slides here, and Sutton and Barto Chapters 13
Sep 17 Deep RL Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Mastering the game of Go without human knowledge
  Part 2: Alternatives to Solving Unknown MDPs  
Sep 19 Model Building PILCO: A Model-Based and Data-Efficient Approach to Policy Search

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Sep 24 Model Building Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

SPNets: Differentiable Fluid Dynamics for Deep Neural Networks
Sep 26 Imitation Learning A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

End-to-End Training of Deep Visuomotor Policies
Oct 01 Inverse Reinforcement Learning Maximum Entropy Inverse Reinforcement Learning

Apprenticeship Learning via Inverse Reinforcement Learning
Oct 03 Self-Supervised and Unsupervised Learning in Computer Vision Learning Features by Watching Objects Move

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Oct 08 Self-Supervision in Robotics Visual Reinforcement Learning with Imagined Goals

Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours
Oct 10 Exploration Curiosity-driven Exploration by Self-supervised Prediction

Diversity is All You Need: Learning Skills without a Reward Function
Oct 15 Exploration Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

Intrinsic Motivation for Encouraging Synergistic Behavior
Oct 17 Hierarchies Feudal Reinforcement Learning

Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Oct 22 Social Learning Time-Contrastive Networks: Self-Supervised Learning from Video

Learning Navigation Subroutines by Watching Videos
  Part 3: Case Studies  
Oct 24 Navigation Cognitive Mapping and Planning for Visual Navigation

Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing
Oct 29 Navigation Semi-parametric Topological Memory for Navigation

Bayesian Relational Memory for Semantic Visual Navigation
Oct 31 Manipulation Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
Nov 05 Manipulation Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

Learning Task-Oriented Grasping for Tool Manipulation with Simulated Self-Supervision
Nov 07 Hardware and Sensors A Soft Robot that Navigates its Environment through Growth

Nov 12 Multi-task Learning Task2Vec: Task Embedding for Meta-Learning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Nov 14 Modern Deep RL vs Classical Control No class! A Tour of Reinforcement Learning: The View from Continuous Control
Moved to Dec 03
Towards Generalization and Simplicity in Continuous Control
Nov 19 Lessons from Cognitive Science and Psychology The Development of Embodied Cognition: Six Lessons from Babies

Nov 21 Big Data vs Clever Algorithms (a) The Bitter Lesson, (b) Re: A Bitter Lesson

Intelligence without Representation
Dec 03 Modern Deep RL vs Classical Control A Tour of Reinforcement Learning: The View from Continuous Control

Towards Generalization and Simplicity in Continuous Control
Dec 05 Project Presentations  
Dec 10 Project Presentations  


Tentative, certain details may be adjusted based on how the class size evolves.