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.