contactimplicitmpc.jl

Fast contact-implicit model predictive control for robotic systems that make and break contact with their environments.

https://github.com/dojo-sim/contactimplicitmpc.jl

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (12.4%) to scientific vocabulary

Keywords

contact control locomotion model-predictive-control motion-planning optimization robotics
Last synced: 6 months ago · JSON representation

Repository

Fast contact-implicit model predictive control for robotic systems that make and break contact with their environments.

Basic Info
  • Host: GitHub
  • Owner: dojo-sim
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 837 MB
Statistics
  • Stars: 144
  • Watchers: 6
  • Forks: 15
  • Open Issues: 4
  • Releases: 0
Topics
contact control locomotion model-predictive-control motion-planning optimization robotics
Created over 4 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

ContactImplicitMPC.jl

CI codecov

This repository contains algorithms and examples from our paper: Fast Contact-Implicit Model-Predictive Control.

A collection of examples are pre-generated in notebooks with the package, please try: flamingo, pushbot, hopper, and quadruped. Additional notebooks with examples from the paper can be generated.

Installation

ContactImplicitMPC can be added via the Julia package manager (type ]): julia pkg> add ContactImplicitMPC

Flamingo

drawing

PushBot

drawing

Hopper Parkour

drawing

Quadruped with Payload

drawing

Hopper Monte Carlo

drawing

Quadruped Monte Carlo

drawing

Reference Trajectories

The trajectories we track in the examples are generated using contact-implicit trajectory optimization and can be run here.

Simulator

The differentiable simulator is available as a stand-alone package: RoboDojo.jl.

Citing

If you find ContactImplicitMPC useful in your project, we kindly request that you cite the following paper: @article{lecleach2021fast, title={Fast Contact-Implicit Model-Predictive Control}, author={Le Cleac'h, Simon and Howell, Taylor A. and Schwager, Mac and Manchester, Zachary}, journal={arXiv preprint arXiv:2107.05616}, year={2021} } The article is available under Open Access here.

Owner

  • Name: Dojo
  • Login: dojo-sim
  • Kind: organization

GitHub Events

Total
  • Issues event: 3
  • Watch event: 37
  • Issue comment event: 2
  • Fork event: 3
Last Year
  • Issues event: 3
  • Watch event: 37
  • Issue comment event: 2
  • Fork event: 3

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 6
  • Total pull requests: 1
  • Average time to close issues: 2 days
  • Average time to close pull requests: 24 days
  • Total issue authors: 5
  • Total pull request authors: 1
  • Average comments per issue: 2.33
  • Average comments per pull request: 2.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • thowell (2)
  • benthebear93 (1)
  • yuankdou (1)
  • GlenHenshaw (1)
  • JuliaTagBot (1)
  • zixinz990 (1)
Pull Request Authors
  • rejuvyesh (1)
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