Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established, but communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, and how the robot can communicate task goals, comprehend human intent, and take the lead as needed. To achieve this, we formulate a task that requires active force communication and propose a taxonomy that extends existing literature. Also, we conducted a study to observe how humans behave during collaborative manipulation tasks. An important contribution of this work is the novel features based on force-kinematic signals that demonstrate predictive power to recognize symbolic human intent. Further, we show the feasibility of developing a real-time intent classifier based on the novel features and speculate the role it plays in high-level robot controllers for physical Human-Robot Interaction (pHRI). This work provides important steps to achieve more human-like fluid interaction in physical co-manipulation tasks that are applicable and not limited to humanoid, assistive robots, and human-in-the-loop automation.

翻译：物理人际交互（pHHI）涉及多种感官模态的使用。关于通过语音和手势进行沟通的研究已趋成熟，但通过力信号进行交流的机制尚不清晰。本文聚焦于探究人类在通过力信号进行协商时所采用的机制，以及机器人如何沟通任务目标、理解人类意图并在必要时主动引领协作。为此，我们设计了一个需要主动力交流的任务，并提出了一个扩展现有文献的分类体系。同时，我们开展了一项研究以观察人类在协作操控任务中的行为模式。本工作的重要贡献在于：基于力-运动学信号提出了具有预测能力的新特征，用于识别象征性的人类意图。此外，我们展示了基于这些新特征开发实时意图分类器的可行性，并探讨了其在物理人机交互（pHRI）高级机器人控制器中可能发挥的作用。本研究为实现物理协作操控任务中更类人化的流畅交互提供了关键进展，其应用范围涵盖（但不限于）仿人机器人、辅助机器人及人在环自动化系统。