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基于模型的概率轨迹匹配仿真学习
Imitation Learning by Model-based Probabilistic Trajectory Matching |
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| 课程网址: |
http://videolectures.net/machine_deisenroth_imitation_learning/
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| 主讲教师: |
Marc Peter Deisenroth
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| 开课单位: |
帝国理工学院
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| 开课时间: |
2013-08-06
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| 课程语种: |
英语
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| 中文简介: |
高效的技能获取对于创建多功能机器人至关重要。教授机器人新技巧的一种直观方法是使其能够将其行为与教师对手头任务的演示相匹配。这种方法被称为模仿学习。经典的模仿学习方法受到对应问题的困扰，即，当不直接观察教师的动作或者教师和机器人的解剖结构显着不同时。为了解决这种对应问题，我们建议学习一种机器人专用控制器，它可以直接匹配机器人轨迹和演示的机器人轨我们使用来自机器人前向动力学的学习概率模型的长期预测，以通过最小化这些轨迹分布之间的Kullback Leibler发散来匹配预测的轨迹分布与观察到的专家轨迹上的分布。通过用具有复杂动力学的肌腱驱动，顺应性机器人臂模仿人类行为来证明所得方法的力量。
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| 课程简介: |
Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to enable it to match its behavior to a teacher's demonstration of the task at hand. This approach is known as imitation learning. Classical methods of imitation learning suffer from the correspondence problem, i.e., when the actions of the teacher are not directly observed or the anatomy of the teacher and the robot differ substantially. To address the correspondence problem, we propose to learn a robot-specific controller that directly matches robot trajectories with demonstrated ones. We use long-term predictions from a learned probabilistic model of the robot's forward dynamics to match the predicted trajectory distribution with the distribution over observed expert trajectories by minimizing the Kullback-Leibler divergence between these trajectory distributions. The power of the resulting approach is demonstrated by imitating human behavior with a tendon-driven, compliant robotic arm with complex dynamics.
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| 关 键 词: |
技能; 多功能; 模仿学习
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-01-16：chenxin
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| 阅读次数: |
59
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