EAGER: Behavioral Repertoires for Soft Robotics

EAGER：软机器人的行为库

• 批准号: 1939930
• 负责人: John Rieffel
• 金额: $ 5万
• 依托单位: Union College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939930&HistoricalAwards=false
• 关键词: EAGER; Behavioral; Repertoires; Soft; Robotics

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.

软机器人是一种引人注目的新平台，可在非结构化、崎岖和动态环境中与人类一起工作。 然而，到目前为止，很少有软机器人可用于搜索和救援以及灾难响应等场景。 这在一定程度上是由于寻找使软机器人有效移动的方法的挑战。该项目的中心目标是建立软机器人能够自主开发环境特定任务的方法，而很少或没有关于自身能力或周围环境的先验知识。 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高风险、高回报的软机器人领域的独立研究人员，从而发展研究人员社区，降低下一代机器人研究人员的进入门槛。该项目将使用质量多样性算法来有效地和自主地发现有效的软机器人行为，使他们能够鲁棒地，适应复杂环境。 这些技术将使用低成本的动态复杂的基于张力整体的机器人。这项研究的具体目标是深入了解软机器人如何自主探索其能力范围，产生充分利用其动态的多模态行为库，并开发这些机器人可以鲁棒有效地适应其库的方法，以应对损坏和意外的环境变化。在整个过程中，这项工作将涉及大量的基于硬件的验证和测试，使用高速，高分辨率的运动捕捉系统。这个奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Behavioral Repertoires for Soft Tensegrity Robots

软张拉整体机器人的行为库

• DOI: 10.1109/ssci47803.2020.9308218
• 发表时间: 2020
• 期刊: 2020 IEEE Symposium Series on Computational Intelligence (SSCI
• 作者: Doney, Kyle;Petridou, Aikaterini;Karaul, Jacob;Khan, Ali;Liu, Geoffrey;Rieffel, John
• 通讯作者: Rieffel, John