Behavioral Repertoires for Soft Tensegrity Robots

Behavioral Repertoires for Soft Tensegrity Robots
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软张拉整体机器人的行为库

DOI:
10.1109/ssci47803.2020.9308218
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发表时间:
2020
期刊:
2020 IEEE Symposium Series on Computational Intelligence (SSCI
影响因子:
--
通讯作者:
Rieffel, John
Rieffel, John
中科院分区:
--
文献类型:
--
作者:
Doney, Kyle;Petridou, Aikaterini;Karaul, Jacob;Khan, Ali;Liu, Geoffrey;Rieffel, John

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移动软机器人在从城市搜索和救援到行星探测等领域提供了引人注目的应用。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反直觉行为,难以建模或预测。因此,大多数移动软体机器人的行为都是通过经验试错和手工调整来发现的。第二个挑战是,软材料很难以高保真度进行模拟,这导致在尝试发现或优化新行为时存在显着的现实差距。在这项工作中,我们在一个物理软张拉整体机器人上使用了一种无模型运行的质量多样性算法,该算法在没有机器人动力学先验知识和最小人为干预的情况下自主生成行为曲目。所得到的行为表显示了对各种任务有用的独特机车步态的多样性。这些结果有助于为通过现实世界的自动化提高移动软机器人的行为能力提供路线图。
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 counter-intuitive 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's 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.
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