计算机科学
人工智能
计算机视觉
对象(语法)
机械手
机器人
软机器人
夹持器
软质材料
启发式
抓住
运动(物理)
补偿(心理学)
人机交互
工程类
机械工程
心理学
材料科学
精神分析
程序设计语言
纳米技术
作者
Sylvain Abondance,Clark B. Teeple,Robert J. Wood
标识
DOI:10.1109/lra.2020.3007411
摘要
In this letter, we show that soft robotic hands provide a robust means of performing basic primitives of in-hand manipulation in the presence of uncertainty. We first discuss the design of a prototype hand with dexterous soft fingers capable of moving objects within the hand using several basic motion primitives. We then empirically validate the ability of the hand to perform the desired object motion primitives while still maintaining strong grasping capabilities. Based on these primitives, we examine a simple, heuristic finger gait which enables continuous object rotation for a wide variety of object shapes and sizes. Finally, we demonstrate the utility of our dexterous soft robotic hand in three real-world cases: unscrewing the cap of a jar, orienting food items for packaging, and gravity compensation during grasping. Overall, we show that even for complex tasks such as in-hand manipulation, soft robots can perform robustly without the need for local sensing or complex control.
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