Artificial Intelligence and Robotics Research


Colony Robotics, Gary Parker, Ozgur Izmirli, Basar Gulcu '08, Jonathan McLean '08, and Richard Zbeda '06 (University of Pennsylania). An 8x8 foot area has been set aside in the Robotics Lab for future colony robotics research. We are working on developing a power supply for the robots, establishing communication links from the learning system computer to the robot, and implementing an overhead camera for colony observation.

Xpilot-AI, Gary Parker, Michael Probst '10, and Matt Parker (University of Nevada, Reno). Xpilot is an online computer game that models space combatants in a 2D environment. We have determined how to access the client program to be able to allow agents with artificial intelligence to play the game (Xpilot-AI). With proper configuration, these agents look and act just like those controlled by human players. The long-term goal is to have them continually learn as they join games in progress and compete against human players.

The Co-Evolution of Robot Control and Morphology, Gary Parker and Pramod Nathan '06 (Brandeis University). This research involves concurrently evolving the body and the mind of a robot. We are using LEGO Mindstorms for the evolution of full body robots and the ServoBot for the evolution of sensor morphology.

The Co-Evolution of a Team of Cooperative Robots, Gary Parker and Joseph Blumenthal '04 (George Mason University). In this research we are using punctuated anytime learning to evolve the control programs for the individuals in a team of simulated robots. They have a common task (such as pushing a box into the corner) and need to cooperate to successfully accomplish it.

Using Cyclic Genetic Algorithms to Generate Gaits for Hexapod Robots, Gary Parker. In this project we use CGAs (a form of evolutionary computation) to learn walking patterns for six-legged robots. Learning takes place on a model of the robot with the new control programs downloaded to the actual robot for testing.

Using Cyclic Genetic Algorithms to Evolve Multi-Loop Control Programs, Gary Parker, Basar Gulcu '08, Richard Zbeda '06 (University of Pennsylania), and Ramona Georgescu '04 (Boston University). In this research we are expanding the use of CGAs to evolve multi-loop programs. Previous versions were limited to a single loop.

Mini-Robot Construction, Gary Parker. This project involves the development of a six-legged robot that is 1/4 to 3/4 the size of the hexapod used in previous experimentation. This robot will be of potential use in future colony robotics research.

Learning Gaits with a Cyclic Genetic Algorithm and a Four-Legged Robot, Gary Parker. This project involves the design of a four-legged robot with legs having three degrees of freedom (the hexapod robots we use have only two) and its controller. The CGA is being used to learn the control programs needed to produce its gaits.

Punctuated Anytime Learning in Evolutionary Robotics, Gary Parker, Michael Probst '10, and Greg Fedynyshyn '07 (Brandeis University). This research uses periodic tests on the actual robot to test the control programs learned on the robot model by evolutionary computation and improve the learning process by altering the learning algorithm (Fitness Biasing) or changing the model (Co-Evolving Model Parameters).

Emergent Gaits Through the Co-Evolution of Leg Cycles, Gary Parker. Incremental learning used a genetic algorithm to coordinated previously learned leg cycles. In this research, we attempt to have gaits emerge during the co-evolution of the leg cycles.

Dynamic Neural Networks To Control Multi-Legged Robots, Gary Parker. In this research we are designing a neural network controller for a simple multi-legged robot. A genetic algorithm is being used to evaluate and improve the NN system in a computer simulation. This control system is being tested on real robots (hexapod and/or octopod).

Neural Networks Implemented on PIC Chips, Gary Parker, Richard Zbeda '06 (University of Pennsylvania), and Pramod Nathan '06 (Brandeis University). We have are working on the implementation of a neural network on PIC chips. Each chip will have a single neuron and the program needed to learn using backpropigation. Initially we will be using 3 chips to create a neural network that can solve the AND, OR, and XOR. In future work, we hope to use it for robot control.

General Areas of Research

Evolutionary Robotics
Adaptive Learning Systems for Autonomous Robot Control
Gait Generation for Multi-Legged Robots
Cyclic Genetic Algorithms
Punctuated Anytime Learning
Co-Evolving Cooperative Teams of Robots


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