Robot introspection with Bayesian nonparametric vector autoregressive hidden Markov models
Robot introspection with Bayesian nonparametric vector autoregressive hidden Markov models
复制标题
贝叶斯非参数向量自回归隐马尔可夫模型的机器人内省
DOI：
10.1109/humanoids.2017.8246976
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Juan Rojas
中科院分区：
文献类型：
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作者：
Hongmin Wu;Hongbin Lin;Y. Guan;K. Harada;Juan Rojas
机器人内省，与过程监控中典型的异常检测相反，有助于机器人了解它在任何时候都在做什么。机器人应该能够识别其动作，不仅当失败或新奇发生时，而且当它执行任何数量的子任务时。随着机器人继续寻求在非结构化环境中发挥作用，他们必须了解他们实际上在做什么来使他们更加强大。这项工作研究了马尔可夫切换过程的贝叶斯非参数技术的建模能力，学习复杂的动态典型的机器人接触任务。我们研究是否马尔可夫切换过程，连同贝叶斯先验可以胜过建模能力的同行：HMM与贝叶斯先验和没有。这项工作进行了测试，在一个卡扣组装任务的特点是高弹性力。该任务由一个具有非常复杂动态的插入子任务组成。我们的方法表现出更强的泛化能力，并且能够以计算高效的方式更好地对具有复杂动态的子任务进行建模。建模技术还用于学习不断增长的机器人技能库，当与低级别控制集成时，允许机器人在线决策。补充信息可以在[1]中找到。
Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making. Supplemental info can be found at [1].