Human-to-robot skill transfer using the SPORE approximation

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 performs the desired task a number of times while his/her input/output pairs are being recorded by the robot. The robot then uses this recorded data to construct a mapping 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 transfer 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 creating the robot's mapping from sensor inputs to actuator outputs.