Learning elementary movements jointly with a higher level task
Learning elementary movements jointly with a higher level task
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与更高级别的任务一起学习基本动作
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Jan Peters
中科院分区：
文献类型：
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作者：
Jens Kober;Jan Peters
许多运动技能由许多较低水平的基本动作组成，这些动作需要排序才能完成任务。为了学习这样的任务，需要同时获得原始动作和高级策略。相比之下，大多数学习方法要么专注于学习联合收割机组合一组固定的选项，要么只学习单个选项。在本文中，我们讨论了一种新的方法，允许提高性能的低级别的行动，同时追求更高级别的任务。所提出的方法是适用于学习更广泛的运动技能，但在本文中，我们采用它学习游戏，玩家想要提高他的表现在游戏的个人行动，同时仍然表现良好的战略水平的游戏。我们建议使用成本正则化核回归来学习较低级别的动作，使用策略迭代的形式来学习较高级别的动作。这两种方法通过它们的转移概率耦合。我们评估的方法，侧失速式投掷游戏在模拟和一个真实的BioRob。
Many motor skills consist of many lower level elementary movements that need to be sequenced in order to achieve a task. In order to learn such a task, both the primitive movements as well as the higher-level strategy need to be acquired at the same time. In contrast, most learning approaches focus either on learning to combine a fixed set of options or to learn just single options. In this paper, we discuss a new approach that allows improving the performance of lower level actions while pursuing a higher level task. The presented approach is applicable to learning a wider range motor skills, but in this paper, we employ it for learning games where the player wants to improve his performance at the individual actions of the game while still performing well at the strategy level game. We propose to learn the lower level actions using Cost-regularized Kernel Regression and the higher level actions using a form of Policy Iteration. The two approaches are coupled by their transition probabilities. We evaluate the approach on a side-stall-style throwing game both in simulation and with a real BioRob.