Joint action understanding improves robot-to-human object handover
Joint action understanding improves robot-to-human object handover
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DOI：
10.1109/iros.2013.6697021
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发表时间：
2013-11
期刊：
影响因子：
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通讯作者：
E. Grigore;K. Eder;A. Pipe;C. Melhuish;U. Leonards
中科院分区：
文献类型：
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作者：
E. Grigore;K. Eder;A. Pipe;C. Melhuish;U. Leonards
开发值得信赖的人类辅助机器人是一项超越传统工程界限的挑战。可信度的基本组成部分是安全性、可预测性和有用性。在本文中，我们证明了从人-人交互到人-机器人上下文的联合动作理解的集成可以显着提高机器人到人对象切换任务的成功率。我们采取两层的方法。第一层处理切换的物理方面。机器人释放物体的决定是由估计移交状态的隐马尔可夫模型通知的。受人与人之间切换观察的启发，我们引入了一个更高级别的认知层，该认知层为人类用户在切换情况下的行为特征建模。特别是，我们专注于包括眼睛凝视/头部方向到机器人的决策。我们的研究结果表明，通过整合这些非语言线索，机器人到人类的成功率可以显着提高，从而产生一个更强大，因此更安全的系统。
The development of trustworthy human-assistive robots is a challenge that goes beyond the traditional boundaries of engineering. Essential components of trustworthiness are safety, predictability and usefulness. In this paper we demonstrate that the integration of joint action understanding from human-human interaction into the human-robot context can significantly improve the success rate of robot-to-human object handover tasks. We take a two layer approach. The first layer handles the physical aspects of the handover. The robot's decision to release the object is informed by a Hidden Markov Model that estimates the state of the handover. Inspired by human-human handover observations, we then introduce a higher-level cognitive layer that models behaviour characteristic for a human user in a handover situation. In particular, we focus on the inclusion of eye gaze / head orientation into the robot's decision making. Our results demonstrate that by integrating these non-verbal cues the success rate of robot-to-human handovers can be significantly improved, resulting in a more robust and therefore safer system.