Self-powered soft robot in the Mariana Trench

Nature ◽  
2021 ◽  
Vol 591 (7848) ◽  
pp. 66-71
Author(s):  
Guorui Li ◽  
Xiangping Chen ◽  
Fanghao Zhou ◽  
Yiming Liang ◽  
Youhua Xiao ◽  
...  
2020 ◽  
Vol 13 (12) ◽  
pp. 121001
Author(s):  
Wei Qu ◽  
Shukun Weng ◽  
Liping Zhang ◽  
Min Sun ◽  
Bo Liu ◽  
...  
Keyword(s):  

2002 ◽  
Author(s):  
Brady Krass ◽  
Charles Hannon ◽  
Joseph Gerstmann
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2016 ◽  
Vol 8 (29) ◽  
pp. 19158-19167 ◽  
Author(s):  
Zhimin Liang ◽  
Pingyang Zeng ◽  
Pengyi Liu ◽  
Chuanxi Zhao ◽  
Weiguang Xie ◽  
...  

2021 ◽  
Vol 163 ◽  
pp. 1773-1785
Author(s):  
Nima Talebzadeh ◽  
Mohsen Rostami ◽  
Paul G. O’Brien

Nano Energy ◽  
2020 ◽  
Vol 72 ◽  
pp. 104742 ◽  
Author(s):  
Yujia Zhong ◽  
Li Zhang ◽  
Vincent Linseis ◽  
Bingchao Qin ◽  
Wenduo Chen ◽  
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Nano Energy ◽  
2021 ◽  
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Junyu Chang ◽  
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Hanqing Liu ◽  
Xiong Zhang ◽  
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Robotica ◽  
2021 ◽  
pp. 1-31
Author(s):  
Andrew Spielberg ◽  
Tao Du ◽  
Yuanming Hu ◽  
Daniela Rus ◽  
Wojciech Matusik

Abstract We present extensions to ChainQueen, an open source, fully differentiable material point method simulator for soft robotics. Previous work established ChainQueen as a powerful tool for inference, control, and co-design for soft robotics. We detail enhancements to ChainQueen, allowing for more efficient simulation and optimization and expressive co-optimization over material properties and geometric parameters. We package our simulator extensions in an easy-to-use, modular application programming interface (API) with predefined observation models, controllers, actuators, optimizers, and geometric processing tools, making it simple to prototype complex experiments in 50 lines or fewer. We demonstrate the power of our simulator extensions in over nine simulated experiments.


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