Skill Acquisition and Use for a Dynamically-Balancing Soccer Robot

Brett Browning, Ling Xu, and Manuela Veloso

Dynamically-balancing robots have recently been made available by Segway LLC, in the form of the Segway RMP (Robot Mobility Platform). We have addressed the challenge of using these RMP robots to play soccer, building up upon our extensive previous work in this multi-robot research domain. In this paper, we make three contributions. First, we present a new domain, called Segway Soccer, for investigating the coordination of dynamically formed, mixed human-robot teams within the realm of a team task that requires real-time decision making and response. Segway Soccer is a game of soccer between two teams consisting of both Segway riding humans and Segway RMPs. We believe Segway Soccer is the first game involving both humans and robots in cooperative roles and with similar capabilities. In conjunction with this new domain, we present our work towards developing a soccer playing robot using the RMP platform with vision as its primary sensor. Our third contribution is that of skill acquisition from a human teacher, where the learned skill is then used seamlessly during robot execution as part of its control hierarchy. Skill acquisition and use addresses the challenge of rapidly developing the low-level actions that are environment dependent and are not transferable across robots.

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