Advanced soft robot modeling in ChainQueen
Name
advanced-soft-robot-modeling-in-chainqueen.pdf
Description
Published version
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
31d5a4925def6fe8a01bf45407b16e5a
Author(s)
Spielberg, Andrew
Du, Tao
Hu, Yuanming
Rus, Daniela
Matusik, Wojciech
Date Issued
2021
Journal
Robotica
Publisher
Cambridge University Press (CUP)
Citation
Spielberg, Andrew, Du, Tao, Hu, Yuanming, Rus, Daniela and Matusik, Wojciech. 2021. "Advanced soft robot modeling in ChainQueen." Robotica.
Version
Final published version
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1017/S0263574721000722
