Incremental learning of full body motion primitives for humanoid robots
Incremental learning of full body motion primitives for humanoid robots
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DOI：
10.1109/ichr.2008.4756000
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
D. Kulić;Dongheui Lee;C. Ott;Yoshihiko Nakamura
中科院分区：
文献类型：
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作者：
D. Kulić;Dongheui Lee;C. Ott;Yoshihiko Nakamura
本文描述了一种基于人体运动观察的在线、增量学习全身运动原语的方法。首先采用随机分割的方法将连续观测序列分割为运动片段。接下来，将运动段增量聚类并组织成代表已知运动原语的分层树结构。使用隐马尔可夫模型对运动原语进行编码，以便同一模型可以用于运动识别和运动生成。同时，通过构造运动原语图来学习运动原语之间的关系。然后，运动原语图可以用来构造由运动原语序列组成的运动。该方法在IRT类人机器人上进行了实现和测试。
This paper describes an approach for on-line, incremental learning of full body motion primitives from observation of human motion. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Motion segments are next incrementally clustered and organized into a hierarchical tree structure representing the known motion primitives. Motion primitives are encoded using hidden Markov models, so that the same model can be used for both motion recognition and motion generation. At the same time, the relationship between motion primitives is learned via the construction of a motion primitive graph. The motion primitive graph can then be used to construct motions, consisting of sequences of motion primitives. The approach is implemented and tested on the IRT humanoid robot.