Shared autonomy policy fine-tuning and alignment for robotic tasks

In this paper, we present a comprehensive shared autonomy framework for human-in-the-loop policy fine-tuning and alignment. Our framework integrates policy adapting algorithms on a multi-agent system foundation tailored for human-robot interaction and decision-making arbitration. This strategy is intended for complex, task-oriented robotic tasks that require cognitive-level human-robot interactions. We design short- and long-horizon fine-tuning algorithms to adapt a policy to different operating conditions and human agents. This is accomplished using Bayesian analysis and custom deep reinforcement learning techniques, through various interaction channels strategically placed at different operational points of the system. To showcase the effectiveness of our algorithms, as well as the strength of our framework, we conduct a human user study involving operation of a laboratory robot in a sequence of high-level pick-and-place tasks. The experiments of the study are designed to demonstrate the interplay between different design elements of our framework, such as, interaction channels and multi-horizon fine-tuning algorithms. By laying out careful hypotheses, we employ objective and subjective metrics to measure the effects of shared autonomy design elements on both the system performance and human user satisfaction. Our human user study reveals significant results related to the complex interplay between shared autonomy design elements, the behavior of the algorithms, and core decision-making and arbitration formulation.