Learning generative models for stylistic whole-body motions and its online adaptation

在这个研究项目中，我们开发了一种新的算法来学习人体全身运动的生成模型，可以明确地捕捉运动序列的风格，并从非平稳无标记的序列观察中估计模型的状态和风格变量。通过运动捕捉数据的实时预测、仿人机器人运动技能的模仿学习以及肌电-机器人界面的学习等应用，证明了所开发算法的适用性和有效性。

In this research project, we developed novel algorithms for learning generative models of a whole-body human motions, that can explicitly capture the style of the motion sequence, and for estimating both state and style variables of the model from non-stationary unlabeled sequential observations. The applicability and effectiveness of the developed algorithms were demonstrated through several applications such as real-time prediction of motion capture data, imitation learning of motor skills by humanoid robots, and learning myoelectric-robot interfaces.