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.

A User Study of a Humanoid Robot as a Social Mediator for Two-Person Conversations

“…With increasing demand for robots for domestic environments, research on human-robot interaction (HRI) has gained more importance. In order to enhance humanrobot interaction, the need for integration of social intelligence in such robots has become a necessity [7][8][9]. Socially intelligent robots should effectively engage with humans and maintain a natural interaction with them over extended periods of time.…”

Monitoring of dyadic conversations : a social signal processing approach

“…With increasing demand of robots for domestic environments, research on humanrobot interaction (HRI) has gained more importance. In order to enhance humanrobot interaction, the need for integration of social intelligence in such robots has become a necessity [56][57][58]. Socially intelligent robots should effectively engage with humans and maintain a natural interaction with them over extended periods of time.…”