论文标题
软式紧张机器人的行为曲目
Behavioral Repertoires for Soft Tensegrity Robots
论文作者
论文摘要
移动软机器人在从城市搜索和救援到行星探索等领域中提供了引人注目的应用程序。软机器人控制的一个关键挑战是，软材料施加的非线性动力学通常会导致复杂的行为，这些行为是违反直觉且难以建模或预测的。结果，大多数移动软机器人的行为都是通过经验试验，错误和手工调整发现的。第二个挑战是，柔软的材料很难以高保真度进行模拟 - 在试图发现或优化新行为时会导致重大的现实差距。在这项工作中，我们采用了一种质量多样性算法在物理柔软的紧张机器人上运行模型，该机器人自主会产生行为曲目，而没有对机器人动力学的先验知识和最少的人类干预。由此产生的行为曲目显示出各种独特的机车步态，可用于各种任务。这些结果有助于通过现实世界自动化提高移动软机器人的行为能力的路线图。
Mobile soft robots offer compelling applications in fields ranging from urban search and rescue to planetary exploration. A critical challenge of soft robotic control is that the nonlinear dynamics imposed by soft materials often result in complex behaviors that are counterintuitive and hard to model or predict. As a consequence, most behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. A second challenge is that soft materials are difficult to simulate with high fidelity -- leading to a significant reality gap when trying to discover or optimize new behaviors. In this work we employ a Quality Diversity Algorithm running model-free on a physical soft tensegrity robot that autonomously generates a behavioral repertoire with no a priori knowledge of the robot dynamics, and minimal human intervention. The resulting behavior repertoire displays a diversity of unique locomotive gaits useful for a variety of tasks. These results help provide a road map for increasing the behavioral capabilities of mobile soft robots through real-world automation.
