Associative motion generation for humanoid robots based on analogy with indication

Satona Motomura, Shohei Kato, H. Itoh
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Abstract

We describe a method of generating new motions associatively from unfamiliar indications. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis (NLPCA) and Jordan recurrent neural network (JRNN). First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using NLPCA. Last, the robot generates a new motion through calculation by JRNN using the associative values. Experimental results demonstrated that our method enabled a humanoid robot, KHR-2HV, to associatively generate some kinds of motion depending on given unfamiliar indications.
基于指示类比的仿人机器人联想运动生成
我们描述了一种从不熟悉的指示联想产生新动作的方法。联想运动生成系统由非线性主成分分析(NLPCA)和Jordan递归神经网络(JRNN)两个神经网络组成。首先,系统使用训练数据学习指示和动作之间的对应关系。其次,使用NLPCA提取关联值,用于从不熟悉的指示中关联新运动。最后,通过JRNN算法计算机器人的关联值,生成新的运动。实验结果表明,我们的方法使类人机器人KHR-2HV能够根据给定的不熟悉的指示联想地产生某些类型的运动。
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