Collaborative manipulation is inherently multimodal, with haptic communication playing a central role. When performed by humans, it involves back-and-forth force exchanges between the participants through which they resolve possible conflicts and determine their roles. Much of the existing work on collaborative human-robot manipulation assumes that the robot follows the human. But for a robot to match the performance of a human partner it needs to be able to take initiative and lead when appropriate. To achieve such human-like performance, the robot needs to have the ability to (1) determine the intent of the human, (2) clearly express its own intent, and (3) choose its actions so that the dyad reaches consensus. This work proposes a framework for recognizing human intent in collaborative manipulation tasks using force exchanges. Grounded in a dataset collected during a human study, we introduce a set of features that can be computed from the measured signals and report the results of a classifier trained on our collected human-human interaction data. Two metrics are used to evaluate the intent recognizer: overall accuracy and the ability to correctly identify transitions. The proposed recognizer shows robustness against the variations in the partner's actions and the confounding effects due to the variability in grasp forces and dynamic effects of walking. The results demonstrate that the proposed recognizer is well-suited for implementation in a physical interaction control scheme.

翻译：协作操作天然具有多模态特性，其中触觉沟通发挥着核心作用。当人类进行协作时，参与者之间会通过来回的力交换来解决潜在冲突并确定各自角色。现有的人机协作操作研究大多假设机器人跟随人类。但要使机器人达到人类搭档的表现水平，它需要能够在适当情况下主动发起并主导协作。为实现这种类人性能，机器人需要具备以下能力：(1) 判断人类意图；(2) 清晰表达自身意图；(3) 选择行动以使双方达成共识。本研究提出了一个基于力交换的协作操作任务中人类意图识别框架。基于人类实验采集的数据集，我们引入了一组可从测量信号中计算出的特征，并报告了在收集的人-人交互数据上训练的分类器结果。采用两个指标评估意图识别器：整体准确率与正确识别状态转换的能力。实验表明，所提出的识别器对搭档动作变化、握力可变性及行走动态效应带来的混杂影响具有鲁棒性。结果证明该识别器非常适合在物理交互控制方案中实施。