Robot Learning Seminar

COURSE LEARNING GOALS
The objective of the class is to:
(1) learn about robotics and control,
(2) understand why machine learning is a necessary tool for building autonomous and intelligent robots,  
(3) get familiar with recent research articles on robot learning,
(4) learn advanced machine learning techniques for robotics and control, and
(5) provide experience in implementing such techniques on representative challenges.

The course is intended for computer science graduate students, who have been exposed to artificial intelligence material in the past. Experience in programming in Python/C++ is needed for a project with a Baxter robot.
INSTRUCTOR
Abdeslam Boularias

OFFICE HOURS 

Abdeslam Boularias: Fridays 2:00-3:00 PM in CBIM 07
Colin Rennie: Wednesdays 10:30-12:00 PM in CBIM

TEACHING ASSISTANTS

TOPICS

The class presents recent developments in machine learning that are related to robotics . Example topics include:

(a) Classical Robotics and Optimal Control: Kinematics, Dynamics, Representing Trajectories, Control in Joint Space and in Task Space
(b) Reinforcement Learning, Learning from Demonstrations, Model Learning
(c) Grasping and Manipulation
(d) Robot Vision

BOOKS
No particular textbook will be used in the course. The material will be primarily based on research papers.

EXPECTED WORK

Regular readings and homework, written and oral presentations, projects.

GRADING SCHEME
Participation: 10%
Written presentation: 15%
Oral presentation: 15%
Homework: 20%
Project: 40%

TENTATIVE SCHEDULE (subject to changes)
Lecture 1 : Introduction and Overview
Lecture 2 : Classical Robotics
Lecture 3 : Optimal Control
Lecture 4 : Machine Learning
Lecture 5 : Model Learning
Lecture 6 : Reinforcement Learning
Lecture 7 : Learning from Demonstrations
Lecture 8 : Policy Search
Lecture 9 : Grasping and Manipulation
Lecture 10 : Robot Vision
Lectures 11-13 : Paper presentations

list of topics for presentations

Topic 1: Deep Reinforcement Learning
Assigned to: Yikun Xian and Xiaoyang Xie
      Topic 2: Active Vision for Object Search
      Assigned to: Zachary Daniels
      Topic 3: Model Learning
      Assigned to: Rasagna Veeramallu and Bharath Joginapally
      Topic 4: Belief Space Planning
      Assigned to: Chaitanya Mitash and Zacharias Psarakis
      Topic 5: Learning to Grasp
      Assigned to:  Vahid Azizi and Changkyu Song
        Topic 6: Learning to Manipulate
        Assigned to: Viraj Malkar and Pritish Sahu
            Topic 7: Learning to Walk
            Assigned to: Koumudhi Cheruku and Lingyi Xu
              Topic 8: Inverse Reinforcement Learning
              Assigned to: Bharadhwaj Vijayaraghava and Yichen Yue
                  Topic 9: Interactive Segmentation
                  Assigned to: Mayank and Ramakanth Vemula
                  Topic 10: Translating Commands in Natural Language into Plans
                  Assigned to: Abhijith Talakola and Mounika Nakkala
                  Topic 11: High-speed Robots
                  Assigned to: Krishna Acanthi Padmanabhan and Sanjivi Muttena
                  Topic 12: Autonomous Driving
                  Assigned to: Yang Yu and Yue Cao
                  Topic 13: Imitation Learning I
                  Assigned to: Suhas Kurup and Zhilmil Dhillon
                  Topic 14: Imitation Learning II
                  Assigned to: Guangzhi Tang and Kun Wang
                  Topic 15: Robot Reinforcement Learning I
                  Assigned to: Aditya Chukka and Teng Long
                  Topic 16: Robot Reinforcement Learning II
                  Assigned to: Shaojun Zhu and Teng Long
                      Topic 17: Human-Robot Interaction
                      Assigned to: Gang Qiao and Hongyang Yu

                            Presentations date: Friday, May 13th, 2016
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