CAREER: Robust Bipedal Locomotion in Real-World Environments

职业：现实环境中稳健的双足运动

• 批准号: 1255018
• 负责人: Katie Byl
• 金额: $ 40万
• 依托单位: University of California-Santa Barbara
• 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: 2013-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1255018&HistoricalAwards=false
• 关键词: CAREER; Robust; Bipedal; Locomotion; Real

The objective of this Faculty Early Career Development (CAREER) Program grant is to develop tools for analyzing and optimizing quasi-periodic biped gaits for high-dimensional models of both humans and humanoid devices. From watching Olympic athletes to taking a hike along a dirt path, our experiences demonstrate that biped locomotion provides a highly agile and robust means of mobility. However, today's humanoid robots are far less capable than their fictional, Hollywood counterparts. The same inverted-pendulum dynamics that make upright walking highly maneuverable under desired control inputs also make it highly susceptible to destabilization. Unlike classic inverted pendulum problems (for example, a rocket propelled by thrusters at its base stabilized during continuous flight), the process of walking is complicated by the discontinuities of impulsive footsteps that vary in both width and height, resulting in a 'quasi-periodic' gait. In particular, work focuses on the challenges of walking on terrain that is not flat and includes intermittent obstacles. The approach exploits the observation that step-to-step 'snapshots' of the position and velocity states of the joints tend to lie on two-dimensional manifolds within a much higher dimensional state space for such systems. This dimensionality reduction enables the use of machine learning techniques for control policy evaluation and improvement to quantifiably estimate fall rates, energy use, and speed for a given combination of biped walker and stochastic terrain.If successful, this work will provide important tools to analyze and/or optimize a variety of real-world systems. From evaluation of the risk of falling for a stroke survivor who walks with an impaired gait, to the design of smart lower-limb prostheses for injured veterans, the modeling approach developed will provide a means of quantifying reliability for systems with such high dimensionality and complexity that traditional guarantees of stability cannot be made. Additionally, robotics research provides a natural gateway for both K-12 and university STEM education. This project includes several outreach elements to encourage this interest, from hosting on-campus field trips for local FIRST Robotics participants and predominantly Hispanic elementary school students to sponsorship of a new Robotics Club at UC Santa Barbara.

该学院早期职业发展（CAREER）计划的目标是开发工具，用于分析和优化人类和人形设备的高维模型的准周期性步态。 从观看奥运会运动员到沿着土路徒步旅行，我们的经验表明，自行车运动提供了一种高度敏捷和强大的移动方式。 然而，今天的人形机器人远不如好莱坞虚构的机器人。 倒立摆动力学使直立行走在所需的控制输入下高度稳定，但同样也使直立行走高度不稳定。 与经典的倒立摆问题不同（例如，火箭在连续飞行期间由推进器在其底部稳定推进），行走的过程因脉冲脚步的不连续性而变得复杂，这些脚步的宽度和高度都不同，导致“准周期性”步态。 特别是，工作重点是在不平坦的地形上行走的挑战，包括间歇性障碍。 该方法利用的观察，一步一步的“快照”的位置和速度状态的关节往往躺在二维流形上的一个更高的维度的状态空间，这样的系统。 这种降维使得能够使用机器学习技术进行控制策略评估和改进，以量化地估计特定组合的步行者步行者和随机地形的跌倒率、能量使用和速度。如果成功，这项工作将为分析和/或优化各种现实世界的系统提供重要工具。 从评估中风幸存者以受损步态行走的跌倒风险，到为受伤的退伍军人设计智能下肢假肢，所开发的建模方法将为具有如此高的维度和复杂性的系统提供量化可靠性的方法，传统的稳定性保证无法实现。 此外，机器人研究为K-12和大学STEM教育提供了一个自然的门户。 该项目包括几个外展元素，以鼓励这种兴趣，从举办校园实地考察当地第一机器人参与者和主要是西班牙裔小学生在加州大学圣巴巴拉一个新的机器人俱乐部的赞助。