CPS: Synergy: Learning to Walk - Optimal Gait Synthesis and Online Learning for Terrain-Aware Legged Locomotion

CPS：协同：学习行走 - 地形感知腿部运动的最佳步态合成和在线学习

• 批准号: 1544857
• 负责人: Patricio Vela
• 金额: $ 80万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544857&HistoricalAwards=false
• 关键词: CPS; Synergy; Learning; Walk; Optimal; CPS; Synergy; Learning; Walk; Optimal

Legged robots have captured the imagination of society at large, throughentertainment and through the dissemination of research findings. Yet,today's reality of what (bipedal) legged robots can do falls short ofsociety's vision. A big part of the reason is that legged robots areviewed as surrogates for humans, able to go wherever humans can as aidsor as assistants where it might also be too dangerous or risky. It isin the expectation of robustness and walking facility that today'sresearch hits its limits, especially when the terrain has granularproperties. Impeding progress is the lack of a holistic approach to thecyber-physical modeling and control of legged robots. The vision ofthis work is to unite experts in granular mechanics, optimal control,and learning theory in order to define a methodology for advancingcyber-physical systems (CPS) involving a tight coupling of the physical withthe cyber through dynamic interactions that must be learned online. Theproposed work will advance the science of cyber-physical systems by moreexplicitly tying sensing, perception, and computing to the optimizationand control of physical systems whose properties are variable anduncertain. Achieving reliable, adaptive legged locomotion over terrainwith arbitrary granular properties would transform several applicationdomain areas of robotics; e.g., disaster response, agricultural andindustrial robotics, and planetary robotics. More broadly, the sametools would apply to related CPS with regards to terrain awareexoskeleton and rehabilitation prosthetics for persons with missing,non-functional, or injured legs, as well as to energy networks withtime-varying, nonlinear dynamics models.The CPS platform to be studied is that of a bipedal robot locomotingover granular ground material with uncertain physical properties (sand,gravel, dirt, etc.). The proposed work seeks to overcome currentimpediments to reliable legged locomotion over uncertain terrain type,which fundamentally relies on the controlled interaction of the robot'sfeet with the physical environment. The research goal is to improve theperception and control of legged locomotion over granular media for theexpress purpose of achieving robust, adaptive, terrain-aware locomotion.It revolves around the hypothesis that simple models with decentpredictive performance and low computational overhead are sufficient forthe optimal control formulations as the compute-constrained adaptivesubsystem will both learn and classify the peculiarities of the terrainonline. The main research objectives will involve: [1] a validatedco-simulation platform for legged robot movement over granular media;[2] terrain-dependent, stable gait generation and gait transitionstrategies via optimal control; [3] online, compute-constrained learningof granular interactions for adaptation and terrain classification; and[4] validated contributions using experimental testbeds involvingvariable and unknown (to the robot) granular media. Given the highvalue of the robotic platforms and the research with regards to outreachand participation, they will be used as outreach tools and to create neweducational modules for promotion of STEM fields. Further, themulti-disciplinary nature of the work will be highlighted in order toemphasize its importance.

腿部机器人通过娱乐和通过研究结果传播了整个社会的想象力。 然而，当今（双皮亚）腿机器人可以做什么的现实缺乏社会的愿景。 原因的很大一部分是，腿部机器人被视为人类的替代物，能够在人类可以作为艾滋病的任何地方作为助手，因为它也可能太危险或冒险。 当今的研究遇到了稳健性和步行设施的期望，尤其是当地形具有颗粒状的限制时。 阻碍进步的是缺乏对腿部机器人的整体方法进行整体方法。 这项工作的愿景是将颗粒状力学，最佳控制和学习理论的专家团结起来，以定义一种推进细胞生理系统（CPS）的方法，涉及必须通过在线学习的动态交互，涉及物理与网络与网络的紧密耦合。 规定的工作将通过更明确地将感应，感知和计算与对属性是可变且无法确定的物理系统的优化和控制的优化和控制来推动网络物理系统的科学。 在任意颗粒状特性上实现可靠的自适应腿部运动，将改变机器人技术的几个应用领域；例如，灾难响应，农业和工业机器人技术以及行星机器人技术。 更广泛地说，Sametools将适用于相关的CP，涉及缺失，非功能或受伤的腿部的人的地形大量骨骼和修复假体，以及与时间变化的，非线性动力学的能量网络以及能量网络。 ETC。）。 拟议的工作旨在克服当前的中测量，以对不确定的地形类型的可靠的腿部运动，这从根本上依赖于机器人的控制与物理环境的受控相互作用。 研究的目标是改善对颗粒媒体的感受和控制，以实现稳健，适应性，地形意识的手段的表达目的。它围绕以下假说，即具有不错的表现性能和低计算的简单模型，足以使其具有适应性的适应性和对构成的适应性，以构成适应性的适应性。 主要的研究目标将涉及：[1]通过颗粒介质上的机器人运动的验证仿真平台； [2]通过最佳控制，依赖地形的，稳定的步态产生和步态过渡策略； [3]在线，针对适应和地形分类的颗粒状相互作用的构成学习； [4]使用涉及可变性和未知（机器人）颗粒介质的实验测试床进行了验证。 鉴于机器人平台的高价值以及有关外展和参与的研究，它们将被用作外展工具，并创建新的教育模块以促进STEM领域。 此外，为了强调其重要性，他们将强调工作的养生本质。