Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and constitute, hence, the earliest and most reliable behavioral cues for intention estimation. In this study, we present an experiment aimed at analyzing human behavior in simple teleoperated pick-and-place tasks in a simulated scenario and at devising a suitable model for early estimation of the current proximal intention. We show that scan paths are, as expected, heavily shaped by the current intention and that two types of Gaussian Hidden Markov Models, one more scene-specific and one more action-specific, achieve a very good prediction performance, while also generalizing to new users and spatial arrangements. We finally discuss how behavioral and model results suggest that eye movements reflect to some extent the invariance and generality of higher-level planning across object configurations, which can be leveraged by cooperative robotic systems.

中文翻译：

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基于注视的意图估计，用于拾取和放置任务中的共享自主权

共享自主权旨在将机器人控制与人工控制相结合，以执行远程遥控操作。除非机器人首先以快速可靠的方式识别当前的人类意图，否则无法实现这种协作交互，因此可以快速实例化并执行合适的辅助计划。长期以来，众所周知，眼球运动可以高度预测手动任务过程中认知过程的进展，因此构成了意图估计的最早，最可靠的行为线索。在这项研究中，我们提出了一个实验，旨在分析模拟场景中简单的远程操作取放任务中的人类行为，并设计一种合适的模型来对当前的近端意图进行早期估计。我们表明，扫描路径符合预期，在很大程度上受当前意图和两种高斯隐马尔可夫模型的影响，一种是针对特定场景的，另一种针对特定动作的，在获得很好的预测性能的同时，还可以推广到新的用户和空间布置。我们最后讨论行为和模型结果如何表明眼动在某种程度上反映了跨对象配置的高级计划的不变性和普遍性，协作机器人系统可以利用这种不变性和普遍性。