DART

DART (Dynamic Animation and Robotics Toolkit) is an open-source,
research-focused physics engine for robotics, animation, and machine learning.
It provides transparent kinematics, dynamics, collision, and constraint-solving
foundations for users who need more than a black-box simulator. DART uses
generalized coordinates for articulated rigid body systems and Featherstone's
Articulated Body Algorithm for accurate, stable motion dynamics.

Why DART?

Research-grade dynamics — Featherstone algorithms, generalized coordinates, and direct access to dynamics quantities
Easy to start — Python and C++ packages through common package managers, plus reproducible source builds with pixi
Extensible foundations — Math, native collision, constraints, model loading, benchmarks, and tests that support new algorithms and baseline comparisons
Unified model loading — Load URDF, SDF, MJCF, and SKEL models through a single API
Scalable compute roadmap — Cross-platform CPU support today, with roadmap work for multi-core, SIMD, and accelerator backends
Battle-tested ecosystem — Powers Gazebo, research labs, and production systems worldwide, with best-effort support for production use

Quick Start

Python

python
import dartpy as dart

world = dart.World()

# Load a robot from URDF
urdf = dart.io.UrdfParser()
robot = urdf.parse_skeleton("dart://sample/urdf/KR5/KR5 sixx R650.urdf")
world.add_skeleton(robot)

# Simulate 100 steps
for _ in range(100):
    world.step()
    print(f"Positions: {robot.get_positions()}")

C++

The DART 7 C++ simulation facade is available in source builds while the final
public header transaction is still in progress. Use the C++ API shape recorded
in docs/design/simulation_cpp_api.md
for current source-checkout examples; DART 6 C++ snippets remain on the
release-6.* branches for compatibility-line users.

Installation

The quick-start snippets above target the current main branch and DART 7 API.
Until DART 7 package artifacts are published, package managers resolve the
latest published DART 6 artifacts instead. During the 6.17 rollout, some indexes
may still serve 6.16.x until their packages finish publishing; use the file-free
package smoke checks below for the installed package version, or use the source
checkout path for the DART 7 quick starts.

Python (Recommended)

Method	Command
uv (preferred)	`uv add dartpy`
pip	`pip install dartpy`
pixi	`pixi add dartpy`
conda	`conda install -c conda-forge dartpy`

C++

Platform	Command
Cross-platform (recommended)	`pixi add dartsim-cpp` or `conda install -c conda-forge dartsim-cpp`
Ubuntu	`sudo apt install libdart-all-dev`
Arch Linux	`yay -S libdart`
FreeBSD	`pkg install dartsim`
macOS	`brew install dartsim`
Windows	`vcpkg install dartsim:x64-windows`

All distributions →

Current Package Smoke Checks

These snippets create a tiny model in code, so they do not depend on sample data
files being present in the installed package.

Python package

python
import dartpy as dart

world = dart.simulation.World()
skeleton = dart.dynamics.Skeleton("box")
skeleton.createFreeJointAndBodyNodePair()
world.addSkeleton(skeleton)
world.step()
print(f"Positions: {skeleton.getPositions()}")

C++ package

The published C++ package smoke for the compatibility line is maintained on
release-6.*. DART 7 C++ package smoke checks move through
check-dart7-artifacts and the local source-checkout promotion gates until
DART 7 C++ artifacts are published.

Source checkout

bash
pixi install
# Smoke the Python-first DART 7 rigid-body demo surface.
pixi run py-demos -- --scene rigid_body --headless --frames 1

# Open the curated rigid-body GUI verifier.
pixi run py-demos
pixi run py-demos -- --scene rigid_solver_compare

# Capture docked visual evidence for the solver comparison.
pixi run py-demo-capture -- --scene rigid_solver_compare --frames 24 --width 960 --height 540 --show-ui

# Optional C++ World-only companion smoke.
pixi run demos -- --scene rigid_body --headless --frames 1

The full 36-row rigid-body visual verification workflow, Rigid Workflow
search terms, and capture commands are in the
Python demos README.

Documentation

Static Docs: English | 한국어
AI Docs (experimental): DeepWiki for quick codebase Q&A | NotebookLM (Google account required)
Questions: Ask in GitHub Discussions when you want maintainer or community input.

Developer Resources

Developer Onboarding Guide — Architecture, components, and workflows
Contributing Guide — Style guide and contribution process
Project Direction — Research-focused north star and priorities
Living Plans — Active and proposed roadmap items
Background Theory — Dynamics, contact solving, and mathematical foundations
Gazebo Integration — gz-physics integration workflow
Release Roadmap — Compatibility, deprecations, and future plans

Branches

main — Active development targeting DART 7
release-6.17 — Maintenance branch for DART 6 (critical fixes only)

Citation

If you use DART in an academic publication, please consider citing this JOSS Paper:

bibtex
@article{Lee2018,
  doi = {10.21105/joss.00500},
  url = {https://doi.org/10.21105/joss.00500},
  year = {2018},
  publisher = {The Open Journal},
  volume = {3},
  number = {22},
  pages = {500},
  author = {Jeongseok Lee and Michael X. Grey and Sehoon Ha and Tobias Kunz and Sumit Jain and Yuting Ye and Siddhartha S. Srinivasa and Mike Stilman and C. Karen Liu},
  title = {DART: Dynamic Animation and Robotics Toolkit},
  journal = {Journal of Open Source Software}
}