NRI: Small: Dynamic Locomotion: From Humans to Robots via Optimal Control

这项研究的目的是开发算法，可以使机器人和模拟角色像人类一样移动。将研究一系列动态运动任务，包括步行，跑步，起床和攀爬，以及任务变化，如向后行走，以及并发任务，如走路时拿着一杯水。该方法是基于最优控制理论。将分析人体运动，并确定其最佳性能标准。智力优势：运动分析将基于一个新的数学框架，从观察到的运动中推断性能标准成为一个凸优化问题。控制合成将利用新的算法进行实时优化，这些算法能够规划涉及多个接触事件的长运动序列。这些算法依赖于接触物理学的新公式，这些公式更适合于数值优化，以及一种新的物理模拟器，它利用了并行处理的进步。更广泛的影响：这项研究将改变机器人和模拟角色的移动方式。目前，许多机器人控制系统的外观动态运动的开环控制，或被设计为执行一个特定的任务。这项工作将使机器人能够表达更自然和灵活的动作，并使机器人编程更加自动化。由此产生的控制器也将作为人体运动控制的模型。

The objective of this research is to develop algorithms that can make robots and simulated characters move like humans. A range of dynamic locomotion tasks including walking, running, getting up and climbing, as well as task variations such as walking backwards, and concurrent tasks such as holding a cup of water while walking, will be studied. The approach is based on optimal control theory. Human movements will be analyzed, and the performance criteria with respect to which they are optimal will be identified. Algorithms that optimize the same performance criteria will then be developed.Intellectual merit: Movement analysis will be based on a new mathematical framework where inference of performance criteria from observed movements becomes a convex optimization problem. Control synthesis will exploit new algorithms for real-time optimization which are able to plan long movement sequences involving multiple contact events. These algorithms rely on novel formulations of the physics of contact which are more amenable to numerical optimization, as well as a new physics simulator which exploits advances in parallel processing.Broader impact: This research will change how robots and simulated characters move. Currently many robotic control systems with the appearance of dynamic movements are controlled in open loop, or are designed to execute one specific task. This work will enable robots to express more natural and versatile movements, as well as make robot programming more automated. The resulting controllers will also serve as models for human motor control.