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

通过娱乐和研究成果的传播，有腿的机器人吸引了整个社会的想象力。然而，今天的现实（双足）机器人所能做的事情与社会的愿景相差甚远。很大一部分原因是，有腿的机器人被视为人类的替代品，能够作为助手去人类能去的任何地方，在那里可能也太危险或有风险。在对坚固性和步行设施的期望中，今天的研究达到了极限，特别是当地形具有颗粒性时。阻碍进展的是缺乏对有腿机器人的网络物理建模和控制的整体方法。这项工作的愿景是联合颗粒力学、最优控制和学习理论方面的专家，以定义一种推进网络物理系统（CPS）的方法，该系统涉及通过必须在线学习的动态交互将物理与网络紧密耦合。提议的工作将通过更明确地将传感、感知和计算与属性可变和不确定的物理系统的优化和控制联系起来，推进网络物理系统的科学。在具有任意颗粒特性的地形上实现可靠的、自适应的腿部运动将改变机器人的几个应用领域；例如，灾难响应、农业和工业机器人、行星机器人。更广泛地说，同样的工具将适用于与地形感知外骨骼和康复假肢相关的CPS，用于腿部缺失、无功能或受伤的人，以及具有时变非线性动力学模型的能源网络。所研究的CPS平台是一个两足机器人在具有不确定物理性质的颗粒状地面材料（沙子、砾石、污垢等）上移动的平台。提出的工作旨在克服目前在不确定地形类型上可靠的腿部运动的障碍，这从根本上依赖于机器人脚与物理环境的受控相互作用。研究目标是为了实现鲁棒的、自适应的、地形感知的运动，提高在颗粒介质上的腿式运动的感知和控制。它围绕着这样一个假设，即具有良好预测性能和低计算开销的简单模型足以实现最优控制公式，因为计算约束的自适应子系统将在线学习和分类地形的特性。主要的研究目标将包括：b[1]一个经过验证的联合仿真平台，用于腿式机器人在颗粒介质上的运动；基于最优控制的地形依赖稳定步态生成和步态转换策略；[3]在线，计算约束的粒度交互适应和地形分类学习；[4]使用实验试验台验证了涉及变量和未知（对机器人）颗粒介质的贡献。鉴于机器人平台的高价值以及与外展和参与有关的研究，它们将被用作外展工具，并为促进STEM领域创建新的教育模块。此外，将强调工作的多学科性质，以强调其重要性。

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.