Human action learning via hidden Markov model
Human action learning via hidden Markov model
复制标题
DOI：
10.1109/3468.553220
复制
发表时间：
1997-01-01
影响因子：
--
通讯作者：
Chen, CS
中科院分区：
文献类型：
--
作者：
Yang, J;Xu, YS;Chen, CS
为了在合作模式中成功地与人类交互并向人类学习，机器人需要一种用于识别、表征和模仿人类技能的机制。特别地，我们感兴趣的是开发用于识别和模仿简单人类动作的机制，即，在没有感觉反馈的情况下，手动操作中的简单活动。为此，我们已经开发出一种方法来模拟这样的行动，使用隐马尔可夫模型（HMM）表示。针对行为建模中的两个关键问题：行为意图分类和行为技能学习，提出了一种基于行为意图分类和行为技能学习的方法，并详细阐述了该方法的实现过程，为行为建模和行为学习提供了一个框架。该方法可以应用于智能识别的手动操作和高级编程的控制输入的监督控制范式内，以及自动转移人类技能的机器人系统。
To successfully interact with and learn from humans in cooperative modes, robots need a mechanism for recognizing, characterizing, and emulating human skills, In particular, it is our interest to develop the mechanism for recognizing and emulating simple human actions, i.e., a simple activity in a manual operation where no sensory feedback is available. To this end, we have developed a method to model such actions using a hidden Markov model (HMM) representation. We proposed an approach to address two critical problems in action modeling: classifying human action-intent, and leaning human skill, for which we elaborated on the method, procedure, and implementation issues in this paper, This work provides a framework for modeling and learning human actions front observations. The approach can be applied to intelligent recognition of manual actions and high-level programming of control input within a supervisory control paradigm, as well as automatic transfer of human skills to robotic systems.