T. Geng, B. Porr, and F. Wörgötter
International Journal of Robotics Research, 25(3), p 243-259, March 2006, Sage press.
In this paper, we present our design and experiments of a planar biped robot under control of a pure sensor-driven controller. This design has some special mechanical features, e.g., small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robot's natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory-tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction of the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows using a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time during walking. This way RunBot can reach a relative speed of 3.5 leg-lengths per second after only a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking.