Teaching a robot manipulation skills through demonstration

An automated software system has been developed to allow robots to learn a gener-alized motor skill from demonstrations given by a human operator. Data is captured using a teleoperation suit as a task is performed repeatedly on Leonardo, the Robotic Life group's anthropomorphic robot, in different parts of his workspace. Stereo vi-sion and tactile feedback data are also captured. Joint and end effector motions are measured through time, and an improved Mean Squared Velocity [MSV] analysis is performed to segment motions into possible goal-directed streams. Further combina-torial selection of subsets of markers allows final episodic boundary selection and time alignment of tasks. The task trials are then analyzed spatially using radial basis func-tions [RBFs] to interpolate demonstrations to span his workspace, using the object position as the motion blending parameter. An analysis of the motions in the object coordinate space [with the origin defined at the object] and absolute world-coordinate space [with the origin defined at the base of the robot], and motion variances in both coordinate frames, leads to a measure [referred to here as objectivity] of how much an