Learning sensory motor coordination for grasping by a humanoid robot

The paper proposes a method for the learning of Sensory-Motor Coordination (SMC) through the teleoperation of a humanoid robot designed for human-robot interaction. It is argued that SMC in a complex environment must be learned rather than programmed. Schema theory is reviewed as a tool for the description of animal behavior at the level of functional modules and higher. SMC is shown to be necessary for the formation of schema assemblages for the control of behavior. Examples are given of four behavior based robot control architectures that implicitly use SMC-driven schema control. It is shown that while the robots are capable of learning, they all rely, to a certain extent, on ad hoc choices of SMC by the designers. A description of the humanoid robot and the sensors it uses for reaching and grasping is given. The proposed method of learning via teleoperation is described. Sensory data acquired through grasping is presented