让机器人通过语言和动作理解人意图，自然完成物品交接。
Multimodal Human-Intent Modeling for Contextual Robot-to-Human Handovers of Arbitrary Objects
- 结合语音与非语言信号识别用户偏好，动态选物
- 根据用户习惯生成合适抓取姿势和交接动作
- 实测验证可提升人机交互自然度，适合助老助医场景
人机物品交接是辅助机器人在养老、医疗、工厂等场景中发挥作用的关键环节。现有方法多依赖预设目标物品，无法感知用户对交接对象的选择偏好及交接方式的隐含需求，限制了交互自然性。本文提出统一框架，利用人类语音与非语言指令选择杂乱环境中的目标物品，并根据用户显性和隐性偏好生成合适的机器人抓取姿态与柔顺交接轨迹。通过真实世界实验与用户研究，验证了该系统在处理日常物品交接任务中的有效性。结果表明，该方法能有效理解用户意图，实现更自然流畅的人机协作。演示视频见 https://youtu.be/6z27B2INl-s。
原文摘要 · Abstract (English)
Human-robot object handover is a crucial element for assistive robots that aim to help people in their daily lives, including elderly care, hospitals, and factory floors. The existing approaches to solving these tasks rely on pre-selected target objects and do not contextualize human implicit and explicit preferences for handover, limiting natural and smooth interaction between humans and robots. These preferences can be related to the target object selection from the cluttered environment and to the way the robot should grasp the selected object to facilitate desirable human grasping during handovers. Therefore, this paper presents a unified approach that selects target distant objects using human verbal and non-verbal commands and performs the handover operation by contextualizing human implicit and explicit preferences to generate robot grasps and compliant handover motion sequences. We evaluate our integrated framework and its components through real-world experiments and user studies with arbitrary daily-life objects. The results of these evaluations demonstrate the effectiveness of our proposed pipeline in handling object handover tasks by understanding human preferences. Our demonstration videos can be found at https://youtu.be/6z27B2INl-s.
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