Coordination and Learning of Movement in Robotic Systems

关于人类运动协调和学习的主流理论之一指出，复杂的人类运动被组织为简单运动基元的串联，这些运动基元又相对于空间和时间进行缩放以生成更多样化的运动范围。 该项目的目标是提供具有类似运动智能能力的机器： 从基本运动基元的词汇表生成复杂运动的能力，针对给定的期望目标优化运动的能力，以及结合反馈机制以确保在存在不确定性和干扰时正确执行运动的能力。 基于对人类和动物运动的研究，为广泛的机器人系统（例如，机械手和步行机）。 一个“运动编译器”将被开发，给定一个序列的原语，适当地调整序列，并确定最佳的时间和空间缩放相对于一个合适的性能指标。 物理最终产品是一个软件系统，它将机器的运动学结构和动态特性、一组运动基元和期望目标作为输入，并产生最佳协调运动。 开发一个高效可靠的运动编译器的关键是一套功能强大的基于李群和李代数数学的多体系统分析工具。

One of the mainstream theories on human movement coordination and learning states that complex human movements are organized as a concatenation of simpler movement primitives, which are in turn scaled with respect to space and time to generate a more diverse range of movements. The goal of this project is to provide machines with similar motor intelligence capabilities: the ability to generate complex movements from a vocabulary of basic movement primitives, to optimize movements for a given desired objective, and to incorporate feedback mechanisms that ensure proper execution of the movement in the presence of uncertainty and disturbances. Based on studies of human and animal movement, basic movement primitives are identified for a wide range of robotic systems (e.g., manipulators and walking machines). A "motion compiler" will be developed that, given a sequence of primitives, appropriately adjusts the sequence, and determines optimal time and space scalings with respect to a suitable performance measure. The physical end-product is a software system that takes as input a machine's kinematic structure and dynamic properties, a set of movement primitives, and a desired goal, and produces an optimal coordinated movement. Key to developing an efficient and reliable motion compiler is a powerful set of recently developed multibody system analysis tools based on the mathematics of Lie groups and Lie algebras.