T, TH 1:15–2:30 SCI L26
M 1:15–2:45 SCI 256
M 3:00–4:30 SCI 256
Professor: Lisa Meeden
Science Center 243
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.
Goals for the course
Analyze and critically discuss technical, research papers both in
writing and in class.
Understand the fundamental questions in a research field and be able
to compare and contrast different approaches to answering these
Formulate and evaluate a research question in adaptive robotics by
completing a substantial project.
Relate your project to prior research via a review of related literature.
Visually present a clear and accessible summary of a research project using a poster.
Orally present a clear and accessible summary of a research project.
Write a coherent conference-style paper describing and
evaluating a research project.
- Class Participation and Paper Responses: 25%
Each week we
will discuss papers in a seminar style. This is not a lecture-based
course. The class as a whole will generate the course content, and you
need to be present to contribute. Doing a close reading of
the assigned papers and writing responses and questions prior to each
class is essential to preparing for a lively and informed discussion.
- Labs: 30%
During the first half of the semester we
will focus on implementing and experimenting with the models that we
are reading about in the primary literature. In the second half of
the semester we will use lab time to make progress on the
- Project: 45%
You will design a project
related to adaptive robotics. You are strongly encouraged to work
with a partner.
Checkpoint demonstration 5%
If you believe that you need accommodations for a disability,
please contact the Office of Student Disability Services (Parrish
113W) or email email@example.com
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
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.
- Your response must be typed (in 12-point font) and be no longer
than a single page. You will upload a PDF file with your
response using Moodle by
10am on the day the paper is scheduled for discussion. I will
review and comment on your responses before class.
- Your response should provide an overview that describes the
paper's main points. The papers will be too long to give a complete
summary. Instead you should focus on the main themes and overall
conclusions. The challenging aspect of these responses is being able
to distill the essential information in a succinct way.
- Your response should also include three questions or issues
that we can discuss during class. Make sure you have access to your
response during class time, either by bringing a hard copy or by
viewing a digital version.
These questions should include big picture issues, such as:
These questions might also include requests for clarification, such
- In what ways does the mechanism described seem like a good tool for
doing adaptive robotics? What are its advantages/disadvantages?
- Do the paper's conclusions logically follow from the data provided?
- How do the paper's mechanisms and conclusions relate to previous
papers that we have read?
- If the paper describes an experiment, what follow up experiments
should be done and why?
- What is the meaning of some specific terminology? Though you
should try to investigate this on your own as well.
- How does a particular algorithm work? Why is a certain step of an
- What is being shown in a particular figure or table in the paper?
||TOPIC & READING
|1 || |
| ||Introduction to Adaptive Robotics and Neural Networks ||1: Simulating Robots in Jupyter Notebooks |
|2 || |
| ||Developmental Robotics ||2: Neural Network Robot Controllers |
|3 || |
Bryce Wiedenbeck Guest lecturer
| ||No lab |
Lisa attending EpiRob17 Conference
Frank Durgin Guest lecturer
|4 || |
Read Sections 1-3
|Evolving Neural Networks ||3: Evolving Neural Network Controllers |
Read Sections 4-7
|5 || |
| ||Novelty-based vs Objective-based Evolution ||4: Implementing Novelty Search |
|6 || |
| ||Active Vision ||5: Applying Novelty Search |
Melanie Mitchell Guest lecturer
| || |
|7 || |
| ||Curiosity-Driven Learning ||6: Creating and Presenting a Research Poster |
|8 || |
| ||Deep Learning ||Poster rubric |
|9 || |
| ||Reinforcement Learning ||Discuss Project Proposal |
|10 || |
| ||Unsupervised Learning ||Continue Project |
|11 || |
|Demo rubric |
Checkpoint Demos (day 2)
|Checkpoint Demos (day 1) |
|12 || |
| ||Continue Project (meet in lab) ||Continue Project |
|Presentation rubric |
Project Presentations (day 1)
|13 || |
| ||Project Presentations (day 3) ||Project Presentations (day 2) |
| ||Project Presentations (day 4) |
|14 || |
| ||Project Presentations (day 5) ||No lab |