Recognizing Intent in Collaborative Manipulation
Recognizing Intent in Collaborative Manipulation
复制标题
识别协作操作的意图
DOI：
10.1145/3577190.3614174
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zefran, Milos
中科院分区：
文献类型：
--
作者：
Rysbek, Zhanibek;Oh, Ki-Hwan;Zefran, Milos
协作操作本质上是多模态的，触觉交流起着核心作用。当由人类执行时，它涉及参与者之间来回的力量交换，通过这种交换，他们可以解决可能的冲突并确定自己的角色。现有的许多关于人机协作操作的工作都假设机器人跟随人类。但对于一个机器人来说，要想达到人类伴侣的水平，它需要能够主动出击，并在适当的时候发挥领导作用。为了实现这种类似人类的性能，机器人需要具备以下能力：(1)确定人类的意图，(2)清楚地表达自己的意图，(3)选择自己的行动，使二人组达成共识。这项工作提出了一个框架，用于识别使用力交换的协作操作任务中的人类意图。基于在人类研究期间收集的数据集，我们引入了一组可以从测量信号中计算出来的特征，并报告了在我们收集的人机交互数据上训练的分类器的结果。两个指标用于评估意图识别器：整体准确性和正确识别过渡的能力。所提出的识别器对同伴动作的变化以及由于抓取力和行走动态效应的变化而引起的混淆效应具有鲁棒性。结果表明，所提出的识别器非常适合在物理交互控制方案中实现。
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.
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期刊：
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