人类-机器人协同中的多周期空间-时间适应
Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming
AI总结：
本文提出RAPIDDS框架，通过联合适应任务调度和机器人运动扩散模型，提升人类-机器人协同的效率与空间适应性，通过仿真和物理机器人实验验证了其有效性。
摘要：
有效的机器人与人类协同对于在人类工作空间中部署机器人至关重要。然而，由于建模个体化的人类能力和偏好具有挑战性，优化联合人类-机器人计划仍是一个难题。尽管先前研究利用制造领域等多周期结构来学习个体倾向并适应重复交互中的计划，但这些技术通常将任务级和运动级适应分开考虑。任务级方法优化分配和调度，但常忽视近距离场景中的空间干扰；相反，运动级方法关注碰撞避免，但忽略更广泛的任务上下文。本文提出RAPIDDS框架，通过建模个体在多个周期内的空间行为（运动路径）和时间行为（完成任务所需时间）来统一这些方法。RAPIDDS随后联合适应任务计划并引导机器人运动的扩散模型，以最大化效率并最小化接近性，考虑这些个性化模型。我们通过仿真和使用7自由度机器人臂的物理机器人场景进行消融研究，验证了这种双重适应的重要性。最后，我们展示了用户研究（n=32）表明，与非适应系统相比，在效率和接近性等客观指标以及流畅性和用户偏好等主观指标上均有显著的计划改进。查看本文的配套视频：https://youtu.be/55Q3lq1fINs。
英文摘要：
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.
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。
