Analogical Generalization of Actions from Single Exemplars in a Robotic Architecture

Humans are often able to generalize knowledge learned from a single exemplar. In this paper, we present a novel integration of mental simulation and analogical generalization algorithms into a cognitive robotic architecture that enables a similarly rudimentary generalization capability in robots. Specifically, we show how a robot can generate variations of a given scenario and then use the results of those new scenarios run in a physics simulator to generate generalized action scripts using analogical mappings. The generalized action scripts then allow the robot to perform the originally learned activity in a wider range of scenarios with different types of objects without the need for additional exploration or practice. In a proof-of-concept demonstration we show how the robot can generalize from a previously learned pick-and-place action performed with a single arm on an object with a handle to a pick-and-place action of a cylindrical object with no handle with two arms.