Awesome
Tiny Differentiable Simulator
Tiny Differentiable Simulator is a header-only C++ (and CUDA) physics library with zero dependencies.
- Note that the main repository is transfered from google-research to Erwin Coumans
It currently implements various rigid-body dynamics algorithms, including forward and inverse dynamics, as well as contact models based on impulse-level LCP and force-based nonlinear spring-dampers. Actuator models for motors, servos, and Series-Elastic Actuator (SEA) dynamics are implemented.
The entire codebase is templatized so you can use forward- and reverse-mode automatic differentiation scalar types, such as CppAD, Stan Math fvar and ceres::Jet. The library can also be used with regular float or double precision values. Another option is to use the included fix-point integer math, that provide cross-platform deterministic computation.
TDS can run thousands of simulations in parallel on a single RTX 2080 CUDA GPU at 50 frames per second:
https://user-images.githubusercontent.com/725468/135697035-7df34b85-c73e-4739-9a76-dc114ce84c4c.mp4
Multiple visualizers are available, see below.
Bibtex
Please use the following reference to cite this research:
@inproceedings{heiden2021neuralsim,
author = {Heiden, Eric and Millard, David and Coumans, Erwin and Sheng, Yizhou and Sukhatme, Gaurav S},
year = {2021},
title = {Neural{S}im: Augmenting Differentiable Simulators with Neural Networks},
booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
url = {https://github.com/google-research/tiny-differentiable-simulator}
}
Related Papers
- "Inferring Articulated Rigid Body Dynamics from RGBD Video" (IROS 2022 submission) Eric Heiden, Ziang Liu, Vibhav Vineet, Erwin Coumans, Gaurav S. Sukhatme Project Page
- "NeuralSim: Augmenting Differentiable Simulators with Neural Networks" (ICRA 2021) Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme. PDF on Arxiv
- “Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap” (RSS 2020 sim-to-real workshop), Eric Heiden, David Millard, Erwin Coumans, Gaurav Sukhatme. PDF on Arxiv and video
- "Interactive Differentiable Simulation", 2020, Eric Heiden, David Millard, Hejia Zhang, Gaurav S. Sukhatme. PDF on Arxiv
Related Research using TDS by Others
- "Efficient Differentiable Simulation of Articulated Bodies" (ICML 2021) Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin PDF on Arxiv
Getting started
The open-source version builds using CMake and requires a compiler with C++17 support.
mkdir build
cd build
cmake ..
make -j
Examples
For visualization, two options are supported:
OpenGL 3+ Visualization
- tiny_opengl3_app, an OpenGL3 visualizer
This visualizer is native part of this library under src/visualizer/opengl
MeshCat Visualization
- MeshCat, a web-based visualizer that uses WebGL
A C++ ZMQ interface is provided.
Before running the example, install python, pip and meshcat, run the meshcat-server and open the web browser (Chrome is recommended for a good three.js experience.)
pip install meshcat
meshcat-server --open
This should open Chrome at http://localhost:7000/static/
Then compile and run tiny_urdf_parser_meshcat_example in optimized/release build.
URDF files can be loaded using a provided parser based on TinyXML2.
All dependencies for meshcat visualization are included in third_party.
Disclaimer: This is not an official Google product.