EAGER/Collaborative Research: Unlocking Legged Mobility Through Structured Prediction

这一早期概念探索研究补助金(AGER)合作项目将探索一种新的跨学科方法，用于健壮和有能力的腿部机器的运动规划和控制，灵感来自人类在具有挑战性的地形中航行的方式，并使用最初为空间任务开发的计算方法来实施。无论是农业、建筑还是灾难应对，腿所提供的机动性为未来的机器人提供了希望，这些机器人可以去人们去的任何地方，无论是在他们的地方还是在他们的身边。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--就像人类一样--计划和执行有目的的动作，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，他们似乎调整了他们在计划中包括的复杂程度，以适应需求的迫切性。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被更粗略地抽象出来。在这个项目中创建的控制方案将在腿部机器人中应用类似的方法，以实现在远程战略指导下的安全、精确的运动。这一成果将促进国家的繁荣和福利，使腿部机器能够做出保持平衡和避免跌倒所需的快速决定，提高作为急救人员、家庭保健助手、探险者或同事的实际部署的健壮性。该项目将为新的机器人控制范例奠定基础，该范例使用严格的方法对分层抽象进行最优控制，并采用新颖的计算解决方案框架。对分层抽象的最优控制将为控制设计人员提供一个新的工具，使他们能够战略性地加强粗粒度的长期计划和细粒度的短期控制之间的一致性。在腿部机器人中，缺乏管理此类挑战的严格框架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稳健性。在这项工作中，这些独立的方法将统一起来，并获得它们的综合好处。设想的求解器使用新的多次射击公式来降低问题的敏感性，并使用新的准牛顿近似来减少运行时间。作为急切努力的一部分，基础工作将考虑四足动物边界和两足动物奔跑的简化2D模型的控制合成。将研究为这些情况设想的抽象的一般性。必要的控制率将在模拟中评估，并将确定不同算法组件的计算要求的细分。这些数据将是确定该方法的进一步进展的关键，该方法将是未来用作在线控制方法以稳定3D机器人运动的必要方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.