COMP 150: Developmental Robotics

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Fall 2017

Instructor: Jivko Sinapov
Department of Computer Science

Tuesday, Thursday 15:00- 16:30
Classroom: Science and Technology 136
Course Syllabus: [PDF] Course Schedule (TENTATIVE): [PDF]

Office hours

Jivko Sinapov (Instructor)

Time: Wednesdays 1:00-3:00 pm or by appointment
Office: Halligan 213
Email: jsinapov--AT--cs--DOT--tufts--DOT--edu

Srijith Rajeev (Teaching Assistant)

Time: Mondays 1:00-3:00 pm and Wednesdays 12:00-2:00 pm
Office: Halligan 228 A-B
Email: srijith2311 -- AT -- gmail -- DOT -- com

Class Diary (including links to slides and readings) [top]

Final Projects [top]

Important Dates:

Preliminary Project Proposal ''Presentations'': October 10th and 12th

Project Proposal Writeup due: October 26th

Progress Report 1 due: November 16th

Final Project Presentations: TBD

Final Project Report: TBD

Course Overview [top]

This class serves as an introduction to the interdisciplinary field of Developmental Robotics, which crosses the boundaries between robotics, artificial intelligence, and developmental psychology. The goal of the field is to create autonomous robots that are intelligent and adaptable in the real world rather than in very limited domains, situations and environments. The class will focus on representations and algorithms that enable a robot to continuously learn about its physical or social environment through its own interaction with it.

Topics include overview of robotics; robotics cognitive architectures; deep learning for visual and non-visual sensory data; unsupervised, self-supervised, and reinforcement learning in robotics; learning object affordances; and, theories of cognitive development and their applications to robotics. There will be several small homework assignments and one large class project, with the goal of producing work worthy of publication. You will use physics-based robot simulators as well as real robots as part of the final project. By the end of this class you will have an understanding of the current state of the art of the field and will be able to conduct original research within it.

Prerequisites [top]

A strong interest in the question, ``What is intelligence and how can it be implemented in a physical robot?''

For best results take two lectures weekly. Common side effects may include sleepless nights, broken robots, nervousness, and banging head on keyboard. Frequent visits to the instructor and the TA have been shown to alleviate some of those symptoms. Talk to your instructor if this class is right for you.

Text and Website [top]

There is no textbook for this course. Instead, relevant research papers will be initially assigned, and later chosen by the students following their interests.

Robotics and Machine Learning Resources [top]

Robot Operating System Framework:
Installinng ROS in VirtualBox for Max OS X:

scikit-learn: Machine Learning in Python:

Computer Vision Libraries in C++: OpenCV and Point Cloud Library

Related Conferences and Journals [top]

Joint IEEE International Conference on Development and Learning: Proceedings

IEEE RAS International Conference on Humanoid Robots: Proceedings

IEEE/RSJ International Conference on Intelligent Robots and Systems: Proceedings

Conference on Robot Learning (CoRL 2017): Accepted Papers

IEEE Transactions on Autonomous Mental Development

Credits and Similar Courses [top]

This class is heavily inspired by a course on Developmental Robotics taught at Iowa State University by Alexander Stoytchev. Feel free to thank him if you enjoy it.

Academic Dishonesty Policy [top]

You are encouraged to form study groups and discuss the reading materials assigned for this class. You are allowed to discuss the the reading response assignments with your colleagues. However, each student will be expected to write his own response.

Collaboration is expected for the final projects -- as soon as you can, you will form teams of 2-3 members. If you absolutely insist on working alone, I won't stop you but you'll be facing a larger work load. For the final project, you're allowed to (and expected to) use various open-source libraries, published code, algorithms, datasets, etc. In fact, doing anything in robotics from scratch is next to impossible :) As long as you cite everything you use that was developed by someone else, you'll be fine.

IMPORTANT: Cheating, plagiarism, and other academic misconducts will not be tolerated and will be handled according to Tufts' policy on academic dishonesty. According to that policy, if I find any evidence of dishonesty, I am required to report it.

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