EAGER/Collaborative Research: Unlocking Legged Mobility Through Structured Prediction

EAGER/协作研究：通过结构化预测解锁腿部灵活性

• 批准号: 1835013
• 负责人: Ryan Russell
• 金额: $ 7.8万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835013&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Unlocking; Legged

This EArly-concept Grant for Exploratory Research (EAGER) collaborative project will explore a novel interdisciplinary approach to motion planning and control for robust and capable legged machines, inspired by the way that humans navigate challenging terrain, and implemented using computational methods originally developed for space missions. Whether for agriculture, construction, or disaster response, the mobility afforded by legs offers promise for future robots that can go where people go, either in their place or by their side. Yet, for these future robots to succeed in real-world environments they must -- as humans do -- plan and execute purposeful movements, to respond to unexpected obstacles as they arise. Human capabilities in this regard vastly exceed those of modern robots. When humans move through the world, they appear to adjust the level of complexity that they include in their planning to the immediacy of the need. That is, movements that must be executed within the next few moments are visualized in detail, while those that will not occur for some time are abstracted more coarsely. The control scheme created in this project will apply a similar approach in legged robots, to achieve safe, precise movements guided by a long-range strategy. The results will advance the national prosperity and welfare, by enabling legged machines that can make the rapid decisions necessary to keep their balance and avoid falls, improving robustness for practical deployment as first responders, home health aides, explorers, or co-workers.The project will lay the foundation for a new paradigm of robot control that makes use of a rigorous methodology for optimal control over hierarchical abstractions with a novel computational solution framework. Optimal control over hierarchical abstractions will provide a new tool for control designers, allowing them to strategically enforce consistency between coarse-grained long-term plans and fine-grained near-term control. In legged robots, the absence of a rigorous framework for managing such a challenge has 1) prevented practical hardware implementation of full-model trajectory optimization and 2) limited the robustness of control based on simple models. In this work, these separate approaches will be unified and their combined benefits captured. The envisioned solver uses a new multiple-shooting formulation to reduce problem sensitivity and a new quasi-Newton approximation to reduce runtime. Fundamental efforts as part of the EAGER effort will consider control synthesis for simplified 2D models of quadruped bounding and biped running. The generality of envisioned abstractions for these cases will be studied. Necessary control rates will be assessed in simulation, and a breakdown of the computational requirements for different algorithm components will be determined. This data will be critical to identify further advances to the approach that will be necessary for its future use as an online control method to stabilize locomotion in 3D robots.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

EARLY概念探索性研究资助（EAGER）合作项目将探索一种新的跨学科方法，用于运动规划和控制，以实现强大而有能力的腿式机器，其灵感来自人类在具有挑战性的地形中航行的方式，并使用最初为太空任务开发的计算方法来实现。无论是农业、建筑还是灾难应对，腿提供的移动性都为未来的机器人提供了希望，它们可以去人们去的地方，或者在他们的地方，或者在他们的身边。然而，为了让这些未来的机器人在现实世界中取得成功，它们必须像人类一样计划和执行有目的的运动，以应对出现的意外障碍。人类在这方面的能力远远超过现代机器人。当人类在世界上移动时，他们似乎会根据需求的迫切性来调整他们在计划中所包含的复杂程度。也就是说，必须在接下来的几分钟内执行的动作被详细地可视化，而那些在一段时间内不会发生的动作则被更粗略地抽象出来。该项目中创建的控制方案将在腿式机器人中应用类似的方法，以实现由远程策略指导的安全，精确的运动。这些结果将促进国家的繁荣和福利，使腿机器能够做出必要的快速决策，以保持平衡并避免福尔斯，提高作为第一响应者，家庭健康助手，探险家，或共同-该项目将为机器人控制的新范式奠定基础，该范式利用严格的方法对分层抽象进行最佳控制，新的计算解决方案框架。分层抽象的最优控制将为控制设计者提供一种新的工具，使他们能够在粗粒度的长期计划和细粒度的近期控制之间实现战略性的一致性。在腿式机器人中，缺乏用于管理这种挑战的严格框架1）阻止了全模型轨迹优化的实际硬件实现，2）限制了基于简单模型的控制的鲁棒性。在这项工作中，这些不同的方法将被统一起来，并获得其综合效益。设想的求解器使用一个新的多重射击配方，以减少问题的敏感性和一个新的拟牛顿近似，以减少运行时间。作为EAGER工作的一部分，基本工作将考虑四足动物跳跃和跳跃运行的简化2D模型的控制合成。将研究这些情况下设想的抽象的一般性。必要的控制率将在模拟中进行评估，并将确定不同算法组件的计算要求。这些数据对于确定该方法的进一步进展至关重要，该方法未来将用作在线控制方法，以稳定3D机器人的运动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。