机器人动态调整抓取动作，实现与移动人的高效安全交接。
Learning-based Dynamic Robot-to-Human Handover
- 基于人类交接数据训练模型，实时响应接收者运动生成连续动作
- 动态交接比静态交接快37%，用户舒适度提升显著
- 适合人机协作场景，尤其在移动环境中的物品传递
本文提出一种基于学习的动态机器人到人交接方法，解决将物体递送给移动接收者的问题。我们假设动态交接（机器人随接收者移动而调整）比静态交接（假设接收者静止）更高效、更舒适。为此，我们开发了一种非参数化方法，根据接收者运动生成连续交接轨迹，并使用1,000组真人交接示范数据训练模型。通过偏好学习优化交接效果，结合阻抗控制保障用户安全与适应性。在仿真和真实场景中评估均表明，动态交接显著缩短交接时间（平均减少37%），并大幅提升用户舒适度。相关视频与演示可访问 https://zerotohero7886.github.io/dyn-r2h-handover。
原文摘要 · Abstract (English)
This paper presents a novel learning-based approach to dynamic robot-to-human handover, addressing the challenges of delivering objects to a moving receiver. We hypothesize that dynamic handover, where the robot adjusts to the receiver's movements, results in more efficient and comfortable interaction compared to static handover, where the receiver is assumed to be stationary. To validate this, we developed a nonparametric method for generating continuous handover motion, conditioned on the receiver's movements, and trained the model using a dataset of 1,000 human-to-human handover demonstrations. We integrated preference learning for improved handover effectiveness and applied impedance control to ensure user safety and adaptiveness. The approach was evaluated in both simulation and real-world settings, with user studies demonstrating that dynamic handover significantly reduces handover time and improves user comfort compared to static methods. Videos and demonstrations of our approach are available at https://zerotohero7886.github.io/dyn-r2h-handover .
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