For a robot in a dynamic environment, the ability to detect motion is crucial. Motion often indicates areas of the robot's surroundings that are changing, contain another agent, or are otherwise worthy of attention. Although legs are arguably the most versatile means of locomotion for a robot, and thus the best suited to an unknown or changing domain, existing methods for motion detection either require that the robot have wheels or that its walking be extremely slow and tightly constrained. This paper presents a method for detecting motion from a quadruped robot walking at its top speed. The method is based on a neural network that learns to predict optic flow caused by its walk, thus allowing environment motion to be detected as anomalies in the flow. The system is demonstrated to be capable of detecting motion in the robot's surroundings, forming a foundation for intelligently directed behavior in complex, changing environments.

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In Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, 219-231, Berlin, 2007. Springer Verlag.

@InProceedings{fidelman:robocup06, title={Detecting Motion in the Environment with a Moving Quadruped Robot}, author={Peggy Fidelman and Thayne Coffman and Risto Miikkulainen}, booktitle={RoboCup-2006: Robot Soccer World Cup X}, editor={Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi}, address={Berlin}, publisher={Springer Verlag}, pages={219-231}, url="http://nn.cs.utexas.edu/?fidelman:robocup06", year={2007} }