Discovering relevant task spaces using inverse feedback control
Discovering relevant task spaces using inverse feedback control
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DOI：
10.1007/s10514-014-9384-1
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发表时间：
2014-02
影响因子：
3.5
通讯作者：
Nikolay Jetchev;Marc Toussaint
中科院分区：
文献类型：
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作者：
Nikolay Jetchev;Marc Toussaint
通过重复和推广专家行为来学习复杂的技能是机器人学中的一个基本问题。然而，通常的方法没有回答什么是适当的表示来生成特定任务的运动的问题。由于人类专家手动设计任务的运动控制表示非常耗时，我们建议从数据观察的运动轨迹中发现这种结构。受逆最优控制的启发，我们提出了一种新的方法来学习潜值函数，模拟和推广所展示的行为，并发现与任务相关的运动表示。我们在几个具有挑战性的高维任务上测试了我们的方法，称为使用反向反馈控制的任务空间检索(TIC)。TIRE从几个示例动作中学习任务的重要控制维度，并能够强有力地概括到新的情况。
Learning complex skills by repeating and generalizing expert behavior is a fundamental problem in robotics. However, the usual approaches do not answer the question of what are appropriate representations to generate motion for a specific task. Since it is time-consuming for a human expert to manually design the motion control representation for a task, we propose to uncover such structure from data-observed motion trajectories. Inspired by Inverse Optimal Control, we present a novel method to learn a latent value function, imitate and generalize demonstrated behavior, and discover a task relevant motion representation. We test our method, called Task Space Retrieval Using Inverse Feedback Control (TRIC), on several challenging high-dimensional tasks. TRIC learns the important control dimensions for the tasks from a few example movements and is able to robustly generalize to new situations.