Robot Weightlifting By Direct Policy Search
Robot Weightlifting By Direct Policy Search
复制标题
机器人举重 通过直接政策搜索
DOI：
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复制
发表时间：
2001
期刊：
影响因子：
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通讯作者：
A. Barto
中科院分区：
文献类型：
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作者：
M. Rosenstein;A. Barto
本文描述了一种构造机器人运动学习任务的方法。通过设计适当的参数化策略，我们证明了一个简单的搜索算法，加上生物激励的约束，为运动技能的获得提供了一种有效的手段。该框架利用了机器人对人类运动学习中的几个元素的对应：模仿、平衡点控制、运动程序和协同效应。我们证明，通过学习，协调行为出现在关于困难的机器人举重任务的原始、粗略的知识中。
This paper describes a method for structuring a robot motor learning task. By designing a suitably parameterized policy, we show that a simple search algorithm, along with biologically motivated constraints, offers an effective means for motor skill acquisition. The framework makes use of the robot counterparts to several elements found in human motor learning: imitation, equilibrium-point control, motor programs, and synergies. We demonstrate that through learning, coordinated behavior emerges from initial, crude knowledge about a difficult robot weightlifting task.