Elucidation of the adaptive mechanism of intricate human motion imitated by deep reinforcement learning

在我们项目的第一年，我们专注于使用我们新开发的低维表示算法来分析舞蹈动作数据。我们的目标是通过从运动学和动力学的角度对各种舞蹈类型的适应性运动技能、协调模式、运动一致性和复杂性进行更好的理解。我们能够创建计算模型，通过深度强化学习准确地再现舞蹈运动技能，这是一个重大成就。我们的方法使我们能够探索舞蹈的认知和身体方面之间复杂的相互作用，并获得关于舞者如何随着时间的推移发展和完善他们的技能的宝贵见解。

In the first year of our project, we focused on analyzing dance motion data using our newly developed low-dimensional representation algorithm. Our goal was to gain a better understanding of the adaptive motor skills, coordination patterns, motion consistency, and complexity of various dance genres, by examining them from both kinematic and kinetic perspectives. We were able to create computational models that can accurately reproduce dance motor skills through deep reinforcement learning, which was a significant achievement. Our approach allowed us to explore the complex interplay between the cognitive and physical aspects of dance, and to gain valuable insights into how dancers develop and refine their skills over time.