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We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method allows for iterative execution and motion refinement using a forgetting factor. During the iterative motion refinement, a confidence value specifies an area of allowed refinement around the nominal trajectory. A novel method for continuous generation of motions from a hidden Markov...
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...
Optical motion capturing systems are widely used to acquire human beings' motion patterns in humanoid imitation learning research. However, optical motion capturing systems have a restricted movable area. This paper proposes the HMM based mimesis scheme using a monocular camera mounted on a humanoid. This scheme releases the restriction of movable area and enables imitation in daily life environments...
In this paper, a new mimesis scheme is proposed. This scheme enables for a humanoid to imitate human's motion even though the humanoid cannot see human's whole-body motion and the humanoid has not seen the exactly same motion so far. Mimesis framework is based on continuous hidden Markov model. Viterbi algorithm is applied in order to generate more various motion patterns than the number of existing...
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