Human-to-robot skill transfer using the SPORE approximation

Humans are capable of accomplishing a variety of complex tasks in environments which have continu ously changing characteristics Robots however gen erally require highly structured environments which al low the programmer to make a large number of as sumptions making the robot s task easier to encode We propose a framework for programming robotic tasks using human to robot skill transfer We assume that there exists a human expert who can accomplish a task in an unstructured environment by using various sensor displays and controls The human expert per forms the desired task a number of times while her his input output pairs are being recorded by the robot The robot then uses this recorded data to construct a map ping between these sensor inputs and actuator outputs This mapping must be general enough to allow the robot to accomplish the same task in similar but not identical dynamic unstructured environments This paper presents a testbed for human to robot skill trans fer which is based on the teleoperated control of a small mobile robot working in an unstructured environment The skill which is transferred from human to robot is loosely based on the tree tending task a task which was chosen for its inherently unstructured nature The SPORE approximation is proposed as a means for cre ating the robot s mapping from sensor inputs to actu