Class information

Room: Science Center 252
Class: Tuesday, Thursday 1:15–2:30pm
Lab: Friday 3:00–4:00pm
Professor: Lisa Meeden
Office: Science Center 243
Phone: 328-8565
Office hours: I will be available Wednesdays 2-4pm, or you can stop by whenever my door is open.

Introduction

This seminar will explore the topic of adaptive robotics with a special focus on developmental robotics, a newly emerging paradigm of research. The goal of developmental robotics is to create intelligent robots by allowing them to go through a developmental process, rather than being directly programmed to solve a particular task. By endowing a robot with an appropriate initial control architecture and adaptive mechanisms, it can learn through continual interactions with the world, developing self-organized knowledge about itself and its environment. We will be studying the following sorts of questions: What should be innate in the robot? What adaptive mechanisms are needed? What motivates the robot to act?

Goals for the course

• Analyze and critically discuss research papers on adaptive robotics both in writing and in class.
• Understand the fundamental questions in the field of adaptive robotics and be able to analyze different solutions to these questions.
• Formulate and evaluate a hypothesis about adaptive robotics by proposing, implementing and testing a project.
• Relate your project to prior research via a review of related literature.
• Orally present a clear and accessible summary of your project.
• Write a coherent, complete paper describing and evaluating your project. You can see examples of past projects at CS81 Spring 2009.

Grading

• Class Participation and Paper Summaries: 20%
  Each Tuesday we will focus on a particular paper from the primary literature. Read the paper prior to class and bring a typed summary of the paper with you to class. You will turn this in at the end of class. The summary must include:
  • A 1-2 paragramph summary of the paper, describing its main points, and why you think this paper is important.
  • A list of questions that we can discuss during class. These may include questions of clarification, but should largely be made up of more interesting questions. These might include: how this paper fits into the field as a whole or how could the experiments be improved or expanded.
At the end of class you will also complete a self-evalutation of your particiaption in the discussion.

Your participation will be evaluated with this form.
• Labs: 15%
  Each Thursday we will focus on hands-on demonstrations. Initially the demonstrations will give you a chance to learn how to control both simulated and physical robots. Later the demonstrations will allow you to experiment with the models we are reading about in the primary literature. This hands-on work will be directly related to the lab assignments. Open lab time will be Friday afternoons. Some lab time will be designated for making progress on the midterm and final projects.

• Midterm Project: 25%
  The goal of this project is to give you a preview, on a smaller scale, of what will be expected on the final project.
  • Checkpoint demonstration 5%
    
  • Paper 20%
    
• Final Project: 40%
  You will be asked to design a project related to adaptive robotics. I encourage you to work in pairs.
• Proposal 5%
  
• Checkpoint demonstration 5%
  
• Presentation 10%
  
• Paper 20%
  

Schedule

WEEK	DAY	ANNOUNCEMENTS	READING
1	Jan 19		Introduction and A neural network primer by Thomas R. Schultz, Chapters 1-2 from Computational Developmental Psychology, MIT Press, 2003.
	Jan 21		Lab1: Learning with neural networks Derivation of BackProp
2	Jan 26		Trends in evolutionary robotics, by Lisa Meeden and Deepak Kumar, Chapter 9 in Soft Computing for Intelligent Robotic Systems, 1998.
	Jan 28	Drop/Add ends (Jan 29)	Lab2: Controlling physical robots
3	Feb 02		Competitive coevolution through evolutionary complexification by Kenneth Stanley and Risto Miikkulainen, Journal of Arificial Intelligence Research, vol. 21, 2004.
	Feb 04		Lab3: Evolving robot controllers with NEAT
4	Feb 09		Evolving 3D morphology and behavior by competition by Karl Sims, in Artificial Live IV Proceedings, eds. Rodney Brooks and Patti Maes, MIT Press, 1994. Automatic design and manufacture of robotic lifeforms by Hod Lipson and Jordan Pollack, Nature, Vol. 406, 2000.
	Feb 11		Midterm Project
5	Feb 16		Learning and evolution by Stefano Nolfi and Dario Floreano, Autonomous Robots, vol. 7.1, 1999.
	Feb 18		Work on Midterm Project
6	Feb 23		Introduction to developmental robotics by Lisa Meeden and Douglas Blank, Connection Science, vol 18.2, 2006. Some basic principles of developmental robotics by Alexander Stoytchev, IEEE Transaction on Autonomous Mental Development, vol 1.2, 2009.
	Feb 25		Midterm Project checkpoint demonstration
7	Mar 02		Developmental robotics: A survey by Max Lungarella, Giorgio Metta, Rolf Pfeifer, and Giulio Sandini, Connection Science, 15:4, 2003.
	Mar 04		Work on Midterm Project
	Mar 09	Spring Break
	Mar 11
8	Mar 16		Intrinsic motivation systems for autonomous mental development by Peirre-Yves Oudeyer, Frederic Kaplan, Verena Hafner, IEEE Transactions on Evolutionary Computation, vol. 11.2, 2007.
	Mar 18		Lab4: Experimenting with IAC
9	Mar 23		A growing neural gas learns topologies by Bernd Fritzke, in Advances in Neural Information Processing Systems 7, 1995. Category-based intrinsic motivation by Rachel Lee, Ryan Walker, Lisa Meeden, and James Marshall, in Proceedings of the Ninth International Conference on Epigenetic Robotics, 2009.
	Mar 25	Last day to declare CR/NC or W (Mar 26)	Lab5: Unsupervised categorization
10	Mar 30		Self-organizing distinctive state abstraction using options by Jefferson Provost, Benjamin Kuipers, and Risto Miikkulainen, in Proceedings of the Seventh International Conference on Epigenetic Robotics, 2007.
	Apr 01		Final Project
11	Apr 06		Foundations of a new science of learning by Andrew Meltzoff, Patricia Kuhl, Javier movellan, and Terrence Sejnowski, Science, July, 2009.
	Apr 08		Work on Final Project
12	Apr 13		Final Project checkpoint demonstration
	Apr 15		Work on Final Project
13	Apr 20		Presentations by: 1. Ryan and Andrew
	Apr 22		Presentations by: 1. Ivana and Sarah 2. Emanne and Ross 3. Ben and Henry
14	Apr 27		Presentations by: 1. Alex 2. Rachael and Serra 3. Zack and Dougal
	Apr 29	Final project paper due (May 14)	Presentations by: 1. Kwame 2. Max 3. Neil and Frank

Final Projects

• An Application of NEAT: Recurrent Neural Networks and a Communication Task, Alex Breslow
• Constraint Facilitated Learning in Artificial Systems, Frank Chien and Wystan N. Palmer
• Implementing SODA in Embodied Robots, Ross Greenwood and Emanne Desjardins
• Interactions Between Intrinsic and Extrinsic Motivation, Max Korein
• Generalization of the Self-Organizing Distinctive State Abstraction, Serra Kornfilt and Rachael Mansbach
• Fitness, Environment, and Input: Evolved Robotics Shepherding, M. Henry Linder and Ben Nye
• GNG-Based Q-Learning, Ivana Ng and Sarah Chasins
• Robotic Curiosity and Memory: Spatial and Implicit Approaches to Time in CBIM, Zack Ontiveros and Dougal Sutherland
• Robot Soccer with GNG and NEAT, Kwame Osei
• Object Detection with AGiNG, Andrew Stromme and Ryan Carlson