Effective information sharing for human-robot collaboration

Abstract

Humans and machines often possess complementary skills. The recognition of this fact is leading to a steadily growing interest in collaborative robots. Despite the growing interest, however, a fundamental question remains to be answered: "How does one develop effective collaborative robots?" Three entities need to be considered while answering this question -- namely, the collaborative robot itself, the human teammate whom the robot interacts with, and, equally importantly, the robot developer who is tasked with designing the machine. Each of these entities possesses different information. Effective sharing of this information is essential for developing collaborative robots and achieving fluent collaboration. In this dissertation, I present models and algorithms to enable effective information sharing between the robot, the human, and the developer. I begin by presenting the Agent Markov Model (AMM), a Bayesian model of sequential decision-making behavior, and Constrained Variational Inference (CVI), a hybrid learning algorithm that can learn generative models both from data and domain expertise. By utilizing AMM and CVI, the developer can specify decisionmaking models both for the human teammate and the collaborative robot with reduced labeling effort. Next, I present ADACORL, a framework to generate the collaborative robot's policy for interaction. By leveraging algorithms for planning under uncertainty, ADACORL can generate fluent robot behavior for human-robot collaborative tasks with state spaces significantly larger than prior art (> 1 million states) and short planning times (< 1 s). Finally, I provide an approach for deciding if, when, and what to communicate during human-robot collaboration. Through human-robot interaction studies, I demonstrate that the proposed decision-making approaches result in the effective use of the robot's action and communication capabilities during collaboration with a human teammate.