ChainQueen: a real-time differentiable physical simulator for soft robotics
Name
1810.01054.pdf
Description
Submitted version
Size
2.18 MB
Format
Adobe PDF
Checksum (MD5)
915a898dd40c433fce9c2b19a0507376
Author(s)
Hu, Yuanming
Liu, Jiancheng
Spielberg, Andrew
Tenenbaum, Joshua B
Freeman, William T
Wu, Jiajun
Rus, Daniela L
Matusik, Wojciech
Date Issued
May 2019
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Citation
Hu, Yuanming et al. "ChainQueen: a real-time differentiable physical simulator for soft robotics." IEEE International Conference on Robotics and Automation 2019 (ICRA 2019), May 20-24, 2019, Montreal, Quebec: 6265-71 ©2019 Author(s)
Version
Original manuscript
Abstract
Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Therefore, rigid body simulators and recently their differentiable variants are studied extensively. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and there-fore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. 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). MLS-MPM can simulate deformable objects with collisions and can be seamlessly incorporated into soft robotic systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of inference, control and co-design tasks for soft robotics.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8794333
