changzhengwu/Dojo.jl

A differentiable simulator for robotics

Dojo

A differentiable simulator for robotics

• arXiv preprint: https://arxiv.org/abs/2203.00806
• Python interface: https://github.com/dojo-sim/dojopy
• site: https://sites.google.com/view/dojo-sim

Examples

Simulation

Atlas drop

REx Hopper drop

Astronaut

Friction-cone comparison

• blue = Dojo nonlinear friction cone
• orange = Dojo linear friction cone
• black = MuJoCo nonlinear friction cone
• magenta = MuJoCo linear friction model

Dzhanibekov effect

Tippe top

Pendulum swing-up

Trajectory Optimization

Box

Hopper

Quadruped

Atlas

Cart-pole

Reinforcement Learning

Half Cheetah

Ant

Real-To-Sim

Learning

Learning geometry: Learning friction coefficient:

Toss

Installation

Dojo can be added via the Julia package manager (type ]):

pkg> add Dojo

The latest version can be added by calling:

pkg> add Dojo#main

Citing

@article{howelllecleach2022,
	title={Dojo: A Differentiable Simulator for Robotics},
	author={Taylor, A. Howell and Le Cleac'h, Simon and Kolter, Zico and Schwager, Mac and Manchester, Zachary},
	journal={arXiv preprint arXiv:2203.00806},
	url={https://arxiv.org/abs/2203.00806},
	year={2022}
}

How To Contribute

Please submit a pull request, open an issue, or reach out to: thowell@stanford.edu (Taylor) or simonlc@stanford.edu (Simon)