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ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
[article]
2018
arXiv
pre-print
Preserved Fulltext
In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM) ...
We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables. ...
ACKNOWLEDGMENTS We would like to thank Chenfanfu Jiang, Ming Gao and Kui Wu for the insightful discussions. ...
arXiv:1810.01054v1
fatcat:pnd2a6jk2vbmdgryblf7rvttgq
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
2019
2019 International Conference on Robotics and Automation (ICRA)
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In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM) ...
We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables. ...
ACKNOWLEDGMENTS We would like to thank Chenfanfu Jiang, Ming Gao and Kui Wu for the insightful discussions. ...
doi:10.1109/icra.2019.8794333
dblp:conf/icra/HuLSTF0RM19
fatcat:sceozqextzajpja6lmx6fmwlqu
Advanced soft robot modeling in ChainQueen
2021
Robotica (Cambridge. Print)
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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. ...
To view supplementary material for this article, please visit https://doi.org/10.1017/ S0263574721000722 ...
doi:10.1017/s0263574721000722
fatcat:zgtenagcurel5ci26z6qkn4foe
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
[article]
2021
arXiv
pre-print
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We introduce a new differentiable physics benchmark called PasticineLab, which includes a diverse collection of soft body manipulation tasks. ...
We expect that PlasticineLab will encourage the development of novel algorithms that combine differentiable physics and RL for more complex physics-based skill learning tasks. ...
Yuanming Hu is partly supported by a Facebook research fellowship and an Adobe research fellowship. ...
arXiv:2104.03311v1
fatcat:tmp23xtv65d5niikoes3bcd22m
Computational Robot Design and Customization
2021
Robotica (Cambridge. Print)
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In the simplest case, consider a user drawing a part for 3D printing. ...
These tools may present constraints or new options for how to specify a design, model and simulate it, explore interactions between different subsystems (mechanical, actuation, control, etc.), or fabricate ...
framework to achieve better performance in locomotion and reaching tasks, and they demonstrate how this can be done for soft robots using a differentiable dynamic simulator for compliant designs. ...
doi:10.1017/s0263574720001162
fatcat:ay4456lcnfaljmwzpudfrxmtri
Learning Material Parameters and Hydrodynamics of Soft Robotic Fish via Differentiable Simulation
[article]
2022
arXiv
pre-print
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The high dimensionality of soft mechanisms and the complex physics of fluid-structure interactions render the sim2real gap for soft robots particularly challenging. ...
Although we focus on a specific application for underwater soft robots, our framework is applicable to any pneumatically actuated soft mechanism. ...
Differentiable soft-body simulators Our work is also relevant to the recent developments of robotic simulators, particularly for soft robots. [18] present a differentiable multi-body dynamics solver that ...
arXiv:2109.14855v2
fatcat:5bspxh6vfjcfxd4uiv4mojxxha
Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review
2021
Artificial Life
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By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. ...
We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. ...
Acknowledgments This work was funded by The United Kingdom Engineering and Physical Sciences Research Council (EPSRC) MOTION grant EP/N03211X/2, RoboPatient grant EP/T00519X/1, and grant RG92738 for the ...
doi:10.1162/artl_a_00330
pmid:33493077
fatcat:oyikumsxr5hdjoyqwtr5cufynu
Co-Learning of Task and Sensor Placement for Soft Robotics
2021
IEEE Robotics and Automation Letters
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Index Terms-Soft robot materials and design, soft sensors and actuators, modeling, control, and learning for soft robots, deep learning methods. ...
We evaluate our model and learning algorithm on six soft robot morphologies for various supervised learning tasks, including tactile sensing and proprioception. ...
We employ the open source, physically-based ChainQueen simulator [3] , whose differentiability can be used to solve motion-planning tasks, useful for generating task-driven trajectory datasets. ...
doi:10.1109/lra.2021.3056369
fatcat:cjbigjlo6ncijkm4upqtwkw7bm
DiffPD: Differentiable Projective Dynamics
[article]
2021
arXiv
pre-print
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We present a novel, fast differentiable simulator for soft-body learning and control applications. ...
Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit time-stepping schemes require tiny time steps to avoid ...
Levin, Bo Zhu, and Eftychios Sifakis for their feedback and suggestions on this paper. The duck and cow mesh models in Figs ...
arXiv:2101.05917v3
fatcat:7zvujof7mjc35a23jhlay2szs4
Soft Robots Modeling: a Literature Unwinding
[article]
2021
arXiv
pre-print
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The robotics community has seen an exponential growth in the level of complexity of the theoretical tools presented for the modeling of soft robotics devices. ...
These theoretical foundations are often taken for granted and this lead to an intricate literature that, consequently, has never been the subject of a complete review. ...
The SoftRobots plugin of SOFA, one of the earliest opensource platforms for physics-based simulation, uses FEM to model, simulate, and control soft robots [28] . ...
arXiv:2112.03645v1
fatcat:tkbvr273jjdkhoww74mp3xa5km
DiffPD: Differentiable Projective Dynamics
2022
ACM Transactions on Graphics
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We present a novel, fast differentiable simulator for soft-body learning and control applications. ...
Inspired by Projective Dynamics ( PD ), we present Differentiable Projective Dynamics ( DiffPD ), an efficient differentiable soft-body simulator based on PD with implicit time integration. ...
Levin, Bo Zhu, and Eftychios Sifakis for their feedback and suggestions on this article. ...
doi:10.1145/3490168
fatcat:aneexw3frjbadpekiwqt56x254
DiffTaichi: Differentiable Programming for Physical Simulation
[article]
2020
arXiv
pre-print
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We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators. ...
A light-weight tape is used to record the whole simulation program structure and replay the gradient kernels in a reversed order, for end-to-end backpropagation. ...
DIFFERENTIABLE CONTINUUM MECHANICS FOR ELASTIC OBJECTS [diffmpm] First, we build a differentiable continuum simulation for soft robotics applications. ...
arXiv:1910.00935v3
fatcat:eesc7eajwre2dgukd7m2ag4v4a
Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges
[article]
2021
arXiv
pre-print
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This design solution aims to bring robots closer to invertebrate animals and soft appendices of vertebrate animals (e.g., an elephant's trunk, a monkey's tail). ...
Continuum soft robots are mechanical systems entirely made of continuously deformable elements. ...
Simulators A bottleneck to entering into the field of soft robots control has been the need to implement the simulator of the soft robot. ...
arXiv:2110.01358v1
fatcat:kcykbxwitvgrphuohxdtssrjxm