EAGER: Behavioral Repertoires for Soft Robotics

软机器人是一个引人注目的新平台，可以在非结构化、坚固和动态的环境中与人类一起操作。然而，到目前为止，很少有软机器人可以在搜索和救援以及灾难响应等场景中进行现场部署。这在一定程度上是由于寻找使软机器人有效移动的方法所面临的挑战。该项目的中心目标是建立软机器人可以自主开发环境特定任务库的方法，而对自己的能力或周围环境知之甚少或根本没有先验知识。这些新技术将允许机器人在受损或任务环境发生变化时快速有效地重新训练自己。重要的是，这项工作还将建立一个模型，让本科生参与和发展成为高风险、高回报的软机器人领域的独立研究人员，从而扩大研究人员群体，降低下一代机器人研究人员的进入门槛。具体来说，该项目将使用质量多样性算法来有效和自主地发现有效的软机器人行为，使它们能够在复杂的环境中稳健和自适应地移动。这些技术将使用低成本的动态复杂的基于张拉整体的机器人来开发。本研究的具体目标是深入了解软体机器人如何自主探索其能力范围，产生充分利用其动力学的多模态行为库，并开发方法，使这些机器人能够强大有效地适应其库以响应损伤和意外的环境变化。在整个过程中，这项工作将涉及大量基于硬件的验证和使用高速，高分辨率运动捕捉系统的测试。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。

Soft robots are a compelling new platform for operating alongside humans in unstructured, rugged, and dynamic environments. However, as of yet, very few soft robots are field-deployable in scenarios such as search-and-rescue and disaster response. This is due in part to the challenge of finding ways of making soft robots move effectively. The central aim of this project is to establish methods by which soft robots can autonomously develop environment-specific task repertoires with little or no prior knowledge about their own abilities or the surrounding environment. These new techniques will allow robots to quickly and efficiently retrain themselves when they are damaged or when their task environment changes. Importantly, this work will also establish a model for involving and developing undergraduate students as independent researchers in the high risk, high payoff field of soft robotics, thereby growing the community of researchers and lowering the barriers of entry for the next generation of robotics researchers.Specifically, this project will use of Quality Diversity Algorithms to efficiently and autonomously discover effective soft robotic behaviors that allow them to robustly and adaptively move in complex environments. These techniques will be developed using low-cost dynamically complex tensegrity-based robots. The specific goals of this research are to produce insights into how soft robots can autonomously explore the range of their abilities, producing multimodal repertoires of behaviors that fully leverage their dynamics, and to develop methods by which these robots can robustly and efficiently adapt their repertoires in response to damage and unexpected environmental change. Throughout, this effort will involve substantial hardware-based validation and testing using a high speed, high resolution motion capture system.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.