通过人体姿态预测协作推重物意图，无需力传感器也能高效协同
Context-aware collaborative pushing of heavy objects using skeleton-based intention prediction
- 用图神经网络分析人体动作时序数据，识别协作意图
- 实验显示机器人协助可显著降低人力消耗，提升任务效率
- 适合工业场景中无力传感器的物理人机协作
在物理人机交互中，力反馈是传达人类意图的常用感知方式，广泛应用于阻抗控制以实现人对机器人的直接引导。然而，在被操作物体未配备力传感器的场景下，该方法不可用。本文研究了一种常见工业任务：在摩擦表面协同推拉重物，此时人类通过语言和非语言线索沟通，身体姿态与动作往往传递更多信息。我们提出一种基于骨架的姿态意图预测的上下文感知方法，利用有向图神经网络分析时空人体姿态数据，实现非语言协作物理操作中的意图预测。实验表明，机器人辅助能显著减少人力消耗并提升任务效率。结果表明，将基于姿态的上下文识别融入或替代力传感，可增强机器人决策与控制效率。
原文摘要 · Abstract (English)
In physical human-robot interaction, force feedback has been the most common sensing modality to convey the human intention to the robot. It is widely used in admittance control to allow the human to direct the robot. However, it cannot be used in scenarios where direct force feedback is not available since manipulated objects are not always equipped with a force sensor. In this work, we study one such scenario: the collaborative pushing and pulling of heavy objects on frictional surfaces, a prevalent task in industrial settings. When humans do it, they communicate through verbal and non-verbal cues, where body poses, and movements often convey more than words. We propose a novel context-aware approach using Directed Graph Neural Networks to analyze spatio-temporal human posture data to predict human motion intention for non-verbal collaborative physical manipulation. Our experiments demonstrate that robot assistance significantly reduces human effort and improves task efficiency. The results indicate that incorporating posture-based context recognition, either together with or as an alternative to force sensing, enhances robot decision-making and control efficiency.
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