RI: Medium: Collaborative Research: Trajectory Libraries for Locomotion on Rough Terrain

该项目的目标是开发设计仿人机器人控制系统的方法，以显示人类在人类规模的崎岖地形上移动的能力、健壮性和灵活性，重点是对打滑和绊倒等错误的响应，以及对不规则地形造成的扰动的响应。这项研究的动机是人类的表现与目前机器人的表现之间存在着巨大的差距。该项目使用数据驱动的方法，利用从人那里记录的运动数据来创建这些行为的轨迹库。它开发的算法允许这些库适应机器人控制，并通过内插、重新排序和新环境的优化来推广。该项目还探索了策略选择，模拟了人类在不同情况下使用的策略。该项目将在制造更好的机器人和更好地理解人类方面产生智力影响。该项目将展示更好的机器人性能，以及更准确的人类行为模型和模拟。在这两种情况下，科学出版物都将通过网络上大量的补充材料和数据加以扩充。潜在的应用包括洞察衰老导致的运动和感觉系统的变化如何增加跌倒的风险。该项目可能会开发出改变环境和培训高危人群的方法，以降低跌倒的风险。

The objective of this project is to develop methods to design control systems for humanoid robots that show human levels of competence, robustness and flexibility in locomotion on human-scale rough terrain, focusing on responses to errors such as slipping and tripping, and responses to perturbations caused by irregular terrain. The research is motivated by the large disparity between human performance and current robot performance. The project uses a data-driven approach, utilizing motion data recorded from people to create trajectory libraries of these behaviors. It develops algorithms that allow these libraries to be adapted for robot control and generalized through interpolation, resequencing, and optimization for new environments. The project also explores strategy selection, modeling what strategies humans use in different situations. The project will have intellectual impacts in making better robots and understanding people better. The project will demonstrate better robot performance as well as more accurate models and simulations of human behavior. In both cases scientific publications will be augmented by extensive additional material and data on the web. Potential applications include insight into how the changes in the motor and sensory systems due to aging increase the risk of falling. The project may develop ways to change environments and train at-risk people to reduce the risk of falling.