This seminar will examine ways of making robots be more adaptive. We will investigate methods that allow robots to learn about themselves and their environment by autonomously exploring what they can achieve rather than being told how to behave. We will focus on machine learning approaches including evolutionary robotics, developmental robotics, unsupervised learning, reinforcement learning, and deep learning. This is a discussion-based course that relies on students doing a close reading of assigned research papers and coming to class prepared to actively engage with the material.
If you believe that you need accommodations for a disability, please contact the Office of Student Disability Services (Parrish 113W) or email studentdisabilityservices@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, the Office will issue students with documented disabilities a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact the Office of Student Disability Services as soon as possible. For details about the accommodations process, visit the Student Disability Service Website.
You are also welcome to contact me privately to discuss your academic needs. However, all disability-related accommodations must be arranged through the Office of Student Disability Services.
Each class meeting will typically focus on one paper. Prepare for class by reading the assigned paper and writing a response in the format described below.
WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LAB |
1 | Sep 05 | Introduction to Adaptive Robotics and Neural Networks
| 1: Simulating Robots in Jupyter Notebooks | |
Sep 07 | ||||
2 | Sep 12 | Developmental Robotics
| 2: Neural Network Robot Controllers | |
Sep 14 | ||||
3 | Sep 19 | Bryce Wiedenbeck Guest lecturer |
| No lab Lisa attending EpiRob17 Conference |
Sep 21 | Frank Durgin Guest lecturer | |||
4 | Sep 26 | Read Sections 1-3 | Evolving Neural Networks
| 3: Evolving Neural Network Controllers |
Sep 28 | Read Sections 4-7 | |||
5 | Oct 03 | Novelty-based vs Objective-based Evolution
| 4: Implementing Novelty Search | |
Oct 05 | ||||
6 | Oct 10 | Active Vision
| 5: Applying Novelty Search | |
Oct 12 | Melanie Mitchell Guest lecturer | |||
Oct 17 | Fall Break | |||
Oct 19 | ||||
7 | Oct 24 | Curiosity-Driven Learning
| 6: Creating and Presenting a Research Poster | |
Oct 26 | ||||
8 | Oct 31 | Deep Learning
| Poster rubric Project | |
Nov 02 | ||||
9 | Nov 07 | Reinforcement Learning
| Discuss Project Proposal | |
Nov 09 | ||||
10 | Nov 14 | Unsupervised Learning
| Continue Project | |
Nov 16 | ||||
11 | Nov 21 | Demo rubric Checkpoint Demos (day 2) | Checkpoint Demos (day 1) | |
Nov 23 | Thanksgiving | |||
12 | Nov 28 | Continue Project (meet in lab) | Continue Project | |
Nov 30 | Presentation rubric Project Presentations (day 1) | |||
13 | Dec 05 | Project Presentations (day 3) | Project Presentations (day 2) | |
Dec 07 | Project Presentations (day 4) | |||
14 | Dec 12 | Project Presentations (day 5) | No lab |