Tiny Differentiable Simulator

Tiny Differentiable Simulator is a header-only C++ physics library with zero dependencies.

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.

Examples to exploit the gradients for system identification using Ceres and trajectory generation using the Control Toolbox are provided.

For optional visualization, a MeshCat ZMQ interface is provided and a PyBullet based visualizer.

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:

MeshCat, a web-based visualizer that uses WebGL

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.

PyBullet visualization, a cross-platform solution that visualizes in a native OpenGL window

URDF files can be loaded either throught the PyBullet interface or a provided parser based on TinyXML2. All simulation features are therefore available without requiring PyBullet.

Several examples (see examples/ folder) use either Ceres or Control Toolbox. To install:

Ceres Solver (optional) >1.14.0
Control Toolbox (optional) >3.0.2

Ceres Solver

Check out the installation page from the manual. Make sure the following dependencies are met:

Eigen 3 (install libeigen3-dev)
BLAS and LAPACK via ATLAS >3.10.3 (install libatlas-base-dev)
(optionally) SuiteSparse >5.1.2 (install libsuitesparse-dev)
Google glog and gflags (install libgoogle-glog-dev)

git clone https://github.com/ceres-solver/ceres-solver.git
cd ceres-solver
git checkout v1.14.0
mkdir build
cd build
cmake ..
make
make install

Control Toolbox

It is recommended to first set up BLASFEO and HPIPM for more efficient solvers that can also handle constraints in the optimization problems:

git clone https://github.com/giaf/blasfeo.git
cd blasfeo
mkdir build
cd build
cmake ..
make
make install

git clone https://github.com/giaf/hpipm.git
cd hpipm
mkdir build
cd build
cmake ..
make
make install

Next, install CppAD:

git clone https://github.com/coin-or/CppAD.git
cd CppAD
mkdir build
cd build
cmake ..
make
make install

Then install CppADCodeGen (we don't really need it but CT requires the CPPADCG flag to use the linearizers).

git clone https://github.com/joaoleal/CppADCodeGen.git
cd CppADCodeGen
mkdir build
cd build
cmake ..
make
make install

For the trajectory optimization experiments, the following modules from Control Toolbox are needed:

ct_core
ct_optcon To build these, make sure Eigen and Boost (libboost-dev ~1.65) are installed. Next, issue the following commands:

git clone https://github.com/ethz-adrl/control-toolbox.git
cd ct_core
mkdir build
cd build
cmake .. -DCPPAD=1 -DHPIPM=1
make
make install
cd ../../ct_optcon
mkdir build
cd build
cmake .. -DHPIPM=1
make
make install

MeshCat Visualization

To support visualization through the web-based visualizer MeshCat, the following packages need to be installed:

libzmq

It is recommended to install the binary packages, as shown on the ZeroMQ README file.

cppzmq

git clone https://github.com/zeromq/cppzmq.git
cd cppzmq
git checkout v4.6.0
mkdir build && cd build
cmake ..
make -j
make install

crossguid

git clone https://github.com/graeme-hill/crossguid.git
cd crossguid
git checkout v0.2.2
mkdir build && cd build
cmake ..
make -j
make install

json

git clone https://github.com/nlohmann/json.git
cd json
git checkout v3.7.3
mkdir build && cd build
cmake ..
make -j
make install

base64

git clone https://github.com/eric-heiden/cpp-base64.git
cd cpp-base64
mkdir build && cd build
cmake ..
make -j
make install

MeshCat server

To install the MeshCat server, issue

pip install meshcat

To start the server and open the visualization in the browser, run

meshcat-server --open

This will start MeshCat with ZMQ listening on port 7000 which is the default setting in the C++ MeshCat visualization client.

PyBullet visualization

git clone https://github.com/bulletphysics/bullet3.git
cd bullet3
./build_cmake_pybullet_double.sh
cd build_cmake
make install

To install the visualizer server to be able to run the visualizer window in shared-memory mode, issue

pip install pybullet

If the simulator is running with PyBullet visualization in shared-memory mode, first open the visualizer server using

python -m pybullet_utils.runServer

TinyXML2

To load URDF models without pybullet, our C++-based importer requires TinyXML2 which is installed as follows:

git clone https://github.com/leethomason/tinyxml2.git
cd tinyxml2
mkdir build && cd build
cmake ..
make -j
make install

Disclaimer: This is not an official Google product.

oarriaga / tiny-differentiable-simulator