Understanding human intentions via Hidden Markov Models in autonomous mobile robots
Understanding human intentions via Hidden Markov Models in autonomous mobile robots
复制标题
通过自主移动机器人中的隐马尔可夫模型理解人类意图
DOI：
10.1145/1349822.1349870
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
G. Bebis
中科院分区：
文献类型：
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作者：
Richard Kelley;A. Tavakkoli;Christopher King;M. Nicolescu;M. Nicolescu;G. Bebis
理解意图是人与人之间沟通的一个重要方面，是人类认知系统的重要组成部分。此功能对于涉及代理之间协作或检测可能构成威胁的情况的情况尤其相关。在本文中，我们提出了一种方法，允许机器人根据通过其自身的感觉运动能力获得的经验来检测他人的意图，然后使用这些经验，同时采取应识别其意图的代理的角度。我们的方法使用一种新颖的隐马尔可夫模型公式，旨在对机器人的体验和与世界的交互进行建模。该机器人观察和分析当前场景的能力采用了一种新颖的基于视觉的技术来进行目标检测和跟踪，并使用非参数递归建模方法。我们使用物理嵌入式机器人验证此架构，检测多个人执行各种活动的意图。
Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we propose an approach that allows a robot to detect intentions of others based on experience acquired through its own sensory-motor capabilities, then using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of Hidden Markov Models designed to model a robot's experience and interaction with the world. The robot's capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities.