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

NRI：小：动态运动：通过最优控制从人类到机器人

• 批准号: 1317702
• 负责人: Emanuel Todorov
• 金额: $ 121.77万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317702&HistoricalAwards=false
• 关键词: NRI; Small; Dynamic; Locomotion; Humans

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.

这项研究的目的是开发可以使机器人和模拟角色像人类一样移动的算法。将研究一系列动态运动任务，包括行走、跑步、起身和攀爬，以及诸如倒退行走等任务变化，以及诸如行走时拿着一杯水等并发任务。该方法基于最优控制理论。将分析人体运动，并确定其最佳表现标准。然后将开发优化相同性能标准的算法。智力优点：运动分析将基于一个新的数学框架，其中从观察到的运动推断性能标准成为凸优化问题。控制综合将利用新算法进行实时优化，能够规划涉及多个接触事件的长运动序列。这些算法依赖于更适合数值优化的接触物理的新颖公式，以及利用并行处理进步的新物理模拟器。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，许多具有动态运动外观的机器人控制系统都是开环控制的，或者被设计为执行一项特定任务。这项工作将使机器人能够表达更自然、更灵活的动作，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类运动控制的模型。