Integrating human observer inferences into robot motion planning
Integrating human observer inferences into robot motion planning
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DOI：
10.1007/s10514-014-9408-x
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发表时间：
2014-12-01
影响因子：
3.5
通讯作者：
Srinivasa, Siddhartha
中科院分区：
文献类型：
--
作者：
Dragan, Anca;Srinivasa, Siddhartha
我们的目标是使机器人能够产生适合人机协作和共存的运动。机器人技术中的大多数运动都是纯功能性的，在机器人单独执行任务时是理想的。然而，在协作中，机器人的运动有一个观察者，观察和解释运动。在这项工作中，我们超越了功能性运动，并将观察者的概念引入到运动规划中，这样机器人就可以产生运动，并且注意到它将如何被人类合作者解释。我们将可预见性和易读性形式化为运动的属性，这些属性自然地产生于观察者所做的相反方向的推断，并借鉴了心理学中的动作解释理论。我们提出了基于理性行动原则的这些推论模型，并推导出可预测或可辨认的运动规划约束功能轨迹优化技术。最后，我们提出了在新手用户上测试我们的工作的实验，并讨论了使机器人在复杂情况下在线产生这种运动的剩余挑战。
Our goal is to enable robots to produce motion that is suitable for human-robot collaboration and co-existence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot's motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible. Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations.