Jivko Sinapov (Instructor)
Time: Wednesdays 1:00-3:00 pm or by
Office: Halligan 213
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]
- 10/19 Overview of Reinforcement Learning
- 10/17 Overview of Machine Learning
- 10/12 Preliminary Project Proposal Presentations II
- 10/10 Preliminary Project Proposal Presentations I
Assigned Readings: WEEK 6-7
- 10/5 Affordances II
- 10/3 Affordances
- 9/28 Self-Recognition
Assigned Homework: Homework 2 (Due Thursday Oct 12th by midnight)
- 9/26 Embodiment
Assigned Readings: WEEK 4
- 9/21 Behavior-Based Robotics (II)
- 9/19 Behavior-Based Robotics (I)
Assigned Readings: WEEK 3
- 9/14 Overview of Robotics (III)
- 9/12 Overview of Robotics (II)
Assigned Homework: Homework 1 (Due Thursday Sep 21st by midnight)
Assigned Readings: WEEK 2
- 9/7 Syllabus and Overview of Robotics (I)
Announcment about Northeast Robotics Colloquium: [PDF]
- 9/5 Class Introduction: What is Developmental Robotics?
Assigned Homework: Homework 0 (Due Thursday Sep 7 before class)
Assigned Readings: WEEK 1
Final Projects [top]
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.
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]
scikit-learn: Machine Learning in Python: http://scikit-learn.org/stable/
Related Conferences and Journals [top]
Credits and Similar Courses [top]
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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