人机协同运输中考虑人类偏好不确定性的相互适应
Mutual Adaptation in Human-Robot Co-Transportation with Human Preference Uncertainty
AI总结：
针对人机协同运输中人类偏好参数不确定及适应策略平衡问题，提出统一框架，通过建模偏好概率分布、时变固执度及协调规划模型，结合位姿优化策略，实现相互适应以提升任务性能。
摘要：
相互适应可以通过整合机器人和人类对环境的理解来增强人机协同运输的整体任务性能。虽然人类建模有助于捕捉人类的主观偏好，但存在两个挑战：（i）人类偏好参数的不确定性，以及（ii）需要平衡对人和机器都有利的适应策略。在本文中，我们提出了一个统一的框架来应对这些挑战，并通过相互适应提高任务性能。首先，我们不依赖固定参数，而是通过纳入一系列不确定的人类偏好参数来建模人类选择的概率分布。在此基础上，我们引入时变固执度量和协调规划模型，该模型允许机器人领导团队的轨迹，或者如果人类偏好的路径与机器人的计划冲突且其固执度超过阈值，则机器人转为跟随人类。最后，我们引入一种用于低级控制的位姿优化策略，以减轻人类领导时的不确定行为。为了验证该框架，我们设计并进行了包含二十名人类参与者反馈的研究。然后，通过仿真，我们展示了我们的模型在通过相互适应和位姿优化增强任务性能方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
