Mutual Prediction Model for Predicting Information for Human Motion Generation - IEEE Conference Publication
Mutual Prediction Model for Predicting Information for Human Motion Generation - IEEE Conference Publication
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用于预测人体运动生成信息的相互预测模型 - IEEE 会议出版物
DOI：
10.1109/sii46433.2020.9026182
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Masahiro Furukawa and Taro Maeda
中科院分区：
文献类型：
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作者：
Tomoki Nishimura;Akiyoshi Hara;Hiroki Miyamoto;Masahiro Furukawa and Taro Maeda
作为一种生成人体运动的模型，已经提出了多个成对的正反模型。该模型通过多个正向/反向模型对，利用正向模型产生运动时获得的责任信号，具有运动控制和运动学习功能。然而，该系统假定物体的目标轨迹是已知的。为了在生物诞生后通过与外界的互动进行学习，需要对目标轨迹的生成过程进行建模。在本研究中，我们提出了目标轨迹是感觉预测本身的假设，并提出了控制感觉和运动意图的正反向模型对相互耦合的相互预测模型作为人体运动生成模型。我们还报告了一些实验结果，表明该假设是恰当的。
As a model for generating human motion, multiple paired forward and inverse models have been proposed. This model has motor control and motor learning functions by multiple forward/inverse model pairs using responsibility signal obtained from the generation of motion by the forward model. However, this system assumes that the target trajectory of the body is given. In order to learn through interaction with the outside world after the birth of a creature, the generation process of the target trajectory should be modeled. In this study, we propose a hypothesis in which the target trajectory is the sense prediction itself, and propose the mutual prediction model in which forward/inverse model pairs governing the sense and motion intention are mutually coupled as a human motion generation model. We also report some experimental results indicating appropriateness of the hypothesis.