人間の選好不確実性を考慮した人とロボットの協調運搬における相互適応

原文アブストラクト

Mutual adaptation can enhance overall task performance in human-robot co-transportation by integrating both the robot's and the human's understanding of the environment. While human modeling helps capture humans' subjective preferences, two challenges persist: (i) the uncertainty of human preference parameters and (ii) the need to balance adaptation strategies that benefit both humans and robots. In this paper, we propose a unified framework to address these challenges and improve task performance through mutual adaptation. First, instead of relying on fixed parameters, we model a probability distribution of human choices by incorporating a range of uncertain human preference parameters. Building on this, we introduce a time-varying stubbornness measure and a coordinated planning model, which allows either the robot to lead the team's trajectory or, if a human's preferred path conflicts with the robot's plan and their stubbornness exceeds a threshold, the robot to transition to following the human. Finally, we introduce a pose optimization strategy for low-level control to mitigate the uncertain human behaviors when they are leading. To validate the framework, we design and perform a study with human feedback from twenty human participants. We then demonstrate, through simulations, the effectiveness of our models in enhancing task performance with mutual adaptation and pose optimization.