A reasoning architecture for human-robot joint tasks using physics-,social-, and capability-based logic

Abstract

This work outlines the development of a reasoning architecture that uses physics-, social-, and agent capability-based knowledge to generate manipulation strategies for a dexterous robot. The architecture learns object affordances through human observations, imposed constraints, and hardcoded physics logic. Human observations are also used to develop a unique manipulation repertoire suitable for the robot. Bayesian Networks are then used to probabilistically determine manipulation strategies for the robot to execute. The robot leverages this knowledge during experimental trials where manipulation strategies suggested by the reasoning architecture are shown to perform well during new manipulation tasks.