Effective human-robot teaming is crucial for the practical deployment of robots in human workspaces. However, optimizing joint human-robot plans remains a challenge due to the difficulty of modeling individualized human capabilities and preferences. While prior research has leveraged the multi-cycle structure of domains like manufacturing to learn an individual's tendencies and adapt plans over repeated interactions, these techniques typically consider task-level and motion-level adaptation in isolation. Task-level methods optimize allocation and scheduling but often ignore spatial interference in close-proximity scenarios; conversely, motion-level methods focus on collision avoidance while ignoring the broader task context. This paper introduces RAPIDDS, a framework that unifies these approaches by modeling an individual's spatial behavior (motion paths) and temporal behavior (time required to complete tasks) over multiple cycles. RAPIDDS then jointly adapts task schedules and steers diffusion models of robot motions to maximize efficiency and minimize proximity accounting for these individualized models. We demonstrate the importance of this dual adaptation through an ablation study in simulation and a physical robot scenario using a 7-DOF robot arm. Finally, we present a user study (n=32) showing significant plan improvement compared to non-adaptive systems across both objective metrics, such as efficiency and proximity, and subjective measures, including fluency and user preference. See this paper's companion video at: https://youtu.be/55Q3lq1fINs.

翻译：有效的人机协同对于机器人在人类工作空间中的实际部署至关重要。然而，由于个体化人类能力和偏好的建模困难，优化联合人机规划仍是一项挑战。尽管先前的研究已利用制造等领域的多周期结构来学习个体倾向，并通过重复交互调整规划，但这些技术通常孤立地考虑任务级和运动级自适应。任务级方法优化分配与调度，却常忽视近距离场景中的空间干扰；反之，运动级方法专注于避免碰撞，却忽略了更广泛的任务上下文。本文提出RAPIDDS框架，该框架通过建模个体在多周期中的空间行为（运动路径）和时间行为（完成任务所需时间）来统一上述方法。随后，RAPIDDS联合调整任务调度，并引导机器人运动的扩散模型，以最大化效率并最小化基于这些个体化模型的邻近性。通过仿真中的消融实验以及使用7自由度机器人臂的物理机器人场景，我们证明了这种双重自适应的重要性。最后，我们展示了一项用户研究（n=32），结果显示，与无自适应系统相比，在效率、邻近性等客观指标以及流畅性、用户偏好等主观测量上均实现了显著规划改进。参见本文配套视频：https://youtu.be/55Q3lq1fINs。