EAGER/Collaborative Research: Unlocking Legged Mobility Through Structured Prediction

这个早期概念探索性研究（EAGER）合作项目将探索一种新的跨学科方法，用于鲁棒和有能力的腿式机器的运动规划和控制，其灵感来自于人类在具有挑战性的地形中导航的方式，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，腿提供的机动性为未来的机器人提供了希望，它们可以去人们去的地方，要么代替他们，要么在他们身边。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，计划并执行有目的的动作，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，他们似乎会调整他们在计划中包含的复杂程度，以满足迫切的需求。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抽象得更粗糙。在这个项目中创建的控制方案将在有腿机器人中应用类似的方法，以实现安全，精确的运动，由长期策略指导。研究结果将促进国家的繁荣和福利，使有腿的机器能够快速做出必要的决定，以保持平衡和避免跌倒，提高鲁棒性，用于实际部署，作为第一响应者、家庭健康助手、探险家或同事。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具有新颖计算解决方案框架的分层抽象进行最优控制。对分层抽象的最优控制将为控制设计者提供一种新的工具，允许他们从战略上加强粗粒度长期计划和细粒度近期控制之间的一致性。在有腿机器人中，缺乏一个严格的框架来管理这样的挑战，1)阻碍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，这些单独的方法将被统一起来，并获得它们的综合效益。设想的求解器使用新的多次射击公式来降低问题的灵敏度，并使用新的准牛顿近似来缩短运行时间。作为EAGER工作的一部分，基础工作将考虑四足动物跳跃和两足动物奔跑的简化二维模型的控制综合。将研究这些案例的设想抽象的通用性。在模拟中将评估必要的控制率，并确定不同算法组件的计算需求的细分。这些数据对于确定该方法的进一步进展至关重要，这将是其未来用作稳定3D机器人运动的在线控制方法所必需的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.