Towards Efficient Collaborations with Trust-Seeking Adaptive Robots

Abstract

We are interested in asymmetric human-robot teams, where a human supervisor occasionally takes over control to aid an autonomous robot in a given task. Our research aims to optimize team efficiency by improving the robot's task performance, decreasing the human's workload, and building trust in the team. We envision synergistic collaborations where the robot adapts its behaviors dynamically to optimize efficacy, reduce manual interventions, and actively seek for greater trust. We describe recent works that study two facets of this trust-seeking adaptive methodology: modeling human-robot trust dynamics, and developing interactive behavior adaptation techniques. We also highlight ongoing efforts to combine these works, which will enable future human-robot teams to be maximally trusting and efficient.