A new RoboCup soccer league is being developed, focusing on human-robot interaction. In this league each team consists of both a human player, mounted on a Segway HT scooter, and a robotic version of the Segway; both human and robot players must cooperate to score goals. This paper details the design of our robotic Segway soccer brain-based device (SS-BBD). The SS-BBD control system is based on a large scale neural simulation, whose design is dictated by details from the published literature on vertebrate neuroanatomy, neurophysiology, and psychophysics. The physical device is completely autonomous, and possesses special manipulators for kicking and capturing a full-sized soccer ball. The SS-BBD uses visual and laser rangefinder information to recognize a variety of game related objects, which enables it to perform actions such as capturing the ball, kicking the ball to another player, shooting a goal, and maneuvering safely across the field. The SS-BBD can act autonomously or obey voice commands from the human player. This is an unprecedented level of human-robot teamwork on a soccer field, in that our players are not merely acting autonomously, but also communicate with each other and support each other on the field

Sampling multisensory information and taking the appropriate motor action is critical for a biological organism's survival, but a difficult task for robots. We present a Neurally Organized Mobile Adaptive Device (NOMAD), whose behavior is controlled by a simulated nervous system based on the anatomy and physiology of the vertebrate brain, that is capable of action selection in a real world environment. NOMAD's nervous system consists of an auditory system, a visual system, a taste system, sets of motor neurons capable of triggering behavior, a tracking system driven by visual stimuli, and a value system. The device itself, which moves autonomously, has a CCD camera for vision, microphones for hearing, and a gripper manipulator to pick up and taste objects by measuring the object's conductivity. Similar to a biological organism, NOMAD learns to categorize sensory information from its environment with no prior instruction, associate positive and negative value with this sensory information, and then learn to select the appropriate motor actions. We suggest that this neurobiological approach to action selection may be generalized to other robot systems.