Anticipating human actions for collaboration in the presence of task and sensor uncertainty
Anticipating human actions for collaboration in the presence of task and sensor uncertainty
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DOI：
10.1109/icra.2014.6907165
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Kelsey P. Hawkins;Shray Bansal;Nam N. Vo;A. Bobick
中科院分区：
文献类型：
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作者：
Kelsey P. Hawkins;Shray Bansal;Nam N. Vo;A. Bobick
结构化活动的表示，允许机器人概率推断人类当前正在执行的任务动作，并预测未来的动作将被执行，以及何时发生。我们的目标是使机器人能够在存在不确定的传感和任务模糊性的情况下预测协作动作。系统可以表示多路径任务，其中任务变化可以包含部分排序的动作，甚至可以完全跳过的可选动作。该任务由一个AND-OR树结构表示，从该树结构构造概率图模型。推导出该模型的推理方法，该方法支持机器人的规划和执行系统，该系统试图根据预期的人类空闲时间来最小化成本函数。我们证明了在模拟和实际的人-机器人性能的双向分支装配任务的理论。特别是，我们表明，推理模型可以鲁棒地预测人类的行动，即使在存在不可靠的或嘈杂的检测，因为它的集成的所有传感信息沿着与知识的任务结构。
A representation for structured activities is developed that allows a robot to probabilistically infer which task actions a human is currently performing and to predict which future actions will be executed and when they will occur. The goal is to enable a robot to anticipate collaborative actions in the presence of uncertain sensing and task ambiguity. The system can represent multi-path tasks where the task variations may contain partially ordered actions or even optional actions that may be skipped altogether. The task is represented by an AND-OR tree structure from which a probabilistic graphical model is constructed. Inference methods for that model are derived that support a planning and execution system for the robot which attempts to minimize a cost function based upon expected human idle time. We demonstrate the theory in both simulation and actual human-robot performance of a two-way-branch assembly task. In particular we show that the inference model can robustly anticipate the actions of the human even in the presence of unreliable or noisy detections because of its integration of all its sensing information along with knowledge of task structure.