Advanced Robotics Projects for Undergraduate Students Douglas Blank Bryn Mawr College James Marshall Sarah Lawrence College Deepak Kumar Bryn Mawr College Lisa Meeden Swarthmore College AAAI Spring Symposium 2007 Robots and Robot Venues: Resources for AI Education

Requirements for Advanced Projects ● Unified framework for interacting with physical and simulated robots ● High-level abstractions for sensing and control ● Details of robot hardware and communication hidden from the user ● Easy enough to learn in a semester ● Powerful enough to enable serious experimental work ● Support for a variety of architectures and paradigms

Many Good Frameworks Available ● Player/Stage/Gazebo (University of Southern California) ● ARIA (MobileRobots, Inc.) ● CARMEN (Carnegie-Mellon University) ● MARIE ● Microsoft Robotics Studio ● Pyro (Bryn Mawr College)

Pyro ● Written in Python – Gentle learning curve – Object-oriented for easy code reuse and extension – Allows access to many additional Python libraries ● Powerful robot sensing and control abstractions ● Interacts seamlessly with both physical robots and robot simulators

Pyro: Robots and Simulators Pioneer Khepera AIBO Hemisson Roomba Gazebo simulator Robocup simulator IntelliBrain Pyrobot simulator

Pyro: Libraries for Advanced AI ● Behavior-based control ● Subsumption architecture ● Finite-state machines ● Fuzzy logic ● Vision ● Color blob tracking ● Reinforcement learning ● Localization and mapping ● Neural networks – Backprop and Quickprop – Simple recurrent networks – Self-organizing maps – Cascade-correlation ● Evolutionary algorithms – Genetic algorithms – Genetic programming

Types of Projects ● Emulation projects attempt to achieve the basic goals or results of prior work without necessarily following the same approach ● Replication projects attempt to implement an existing model from the literature ● Extension projects attempt to go beyond an existing model from the literature in interesting ways ● Original research projects attempt to create something new ● Some examples from our own experience follow...

A Tour Guide Robot ● Bryn Mawr College, 2004 ● Finite-state machine brain – Obstacle avoidance – Dead reckoning – Simple landmark detection via color-match filtering – Python-wrapped Festival Server and Sphinx for speech recognition and generation ● Emulated more sophisticated tour-guide projects ● YouTube video clip YouTube video clip

Developmental Robotics AIBO playpen ● Swarthmore College, Spring 2006 ● Upper-level seminar course ● Students studied Oudeyer and Kaplan’s Intelligent Adaptive Curiosity (IAC) model of intrinsic motivation and learning ● 3-week midterm project: – Start with IAC prototype in Python – Design, run, and analyze a novel experiment, and write a paper ● Some students closely replicated the published results ● Others extended and improved the model in various ways

Evolutionary Robotics ● Integrating GAs, neural nets, and robotics is easy in Pyro ● Example: replicating an experiment by Marocco and Nolfi from Alife X, 2006 ● Use a GA to evolve a neural net to control a group of robots ● Robots can emit and hear sounds ● Robots “feed” on 2 light sources ● 2 robots can feed at each light ● Robots evolve a simple “language” ● Currently the focus of a student project at Sarah Lawrence College

Natural Language Interaction ● Bryn Mawr College, senior thesis project in Linguistics/CS ● Develop a natural language interface for a mobile robot – English-language parser – Semantic analyzer – First-order logic engine ● Components: – Scribbler robot from Parallax Corp. – Python-based natural language toolkit NLTK – Myro programming environment (based on Pyro) >>> do you see a wall No >>> move until you see a wall >>> turn right until you don't see a wall to your right >>> do you see a wall to your front No >>> move until you see a wall to your front

Bubbles, the Robotic Blimp ● Bryn Mawr College and Swarthmore College, 2006 ● AAAI 2006 Mobile Robot Scavenger Hunt contest ● Explored problem of 3-D control ● Students used Pyro’s main 2-D control system, but added an additional mechanism for controlling height ● Won a Technical Achievement Award for original hardware design

Neural Network Research ● Pomona College, summer undergrad research project ● Studied “self-predicting” neural networks, using Pyro ● Mixture of learnable and unlearnable patterns ● Self-predicting networks much better at learning task ● Paper presented at AAAI Fall 2005 symposium input internal representation predicted internal representation predicted output error output Time (Epochs) Output error 25% predictable

Conclusion ● Pyro allows students to focus on the conceptual core of a problem without being overwhelmed by technical details ● Students can accomplish more as a result ● Projects are more enjoyable and rewarding ● Many types of advanced projects are possible ● Active development of Pyro continues ● We encourage you to contribute!