References#
Cross-references elsewhere use keys like [Mink]_. To cite mink-warp itself,
use CITATION.cff
(GitHub “Cite this repository”) or the BibTeX below.
Cite mink-warp#
Nedelchev, S., & Domrachev, I. (2026). mink-warp: Batched differential inverse kinematics on MuJoCo Warp.
Cite
@software{nedelchev2026minkwarp,
author = {Nedelchev, Simeon and Domrachev, Ivan},
title = {{mink-warp: Batched differential inverse kinematics on MuJoCo Warp}},
year = {2026},
version = {0.1.0},
url = {https://github.com/simeon-ned/mink-warp},
repository = {https://github.com/simeon-ned/mink-warp},
license = {Apache-2.0},
}
Publications#
- mw-bib-entry
Samson, C., Espiau, B., & Le Borgne, M. (1991). Robot Control: The Task Function Approach. Oxford University Press.
Cite
@book{samson1991robot, title = {Robot Control: The Task Function Approach}, author = {Samson, Claude and Espiau, Bernard and Le Borgne, Michel}, year = {1991}, publisher = {Oxford University Press}, }
Sugihara, T. (2011). Solvability-unconcerned inverse kinematics by the Levenberg–Marquardt method. IEEE Transactions on Robotics, 27(5), 984–991. DOI
Cite
@article{sugihara2011solvability, title = {Solvability-unconcerned inverse kinematics by the {Levenberg--Marquardt} method}, author = {Sugihara, Tomomichi}, journal = {IEEE Transactions on Robotics}, volume = {27}, number = {5}, pages = {984--991}, year = {2011}, }
Solà, J., Deray, J., & Atchuthan, D. (2018). A micro Lie theory for state estimation in robotics. arXiv:1812.01537. arXiv
Cite
@article{sola2018micro, title = {A micro {Lie} theory for state estimation in robotics}, author = {Sol{\`a}, Joan and Deray, Jeremie and Atchuthan, Dennis}, journal = {arXiv preprint arXiv:1812.01537}, year = {2018}, }
Caron, S. (2023). Jacobian of a kinematic task and derivatives on manifolds. Online note
Cite
@misc{caron2023jacobian, author = {Caron, St{\'e}phane}, title = {Jacobian of a kinematic task and derivatives on manifolds}, year = {2023}, howpublished = {\url{https://scaron.info/robotics/jacobian-of-a-kinematic-task-and-derivatives-on-manifolds.html}}, }
All entries#
Software: CITATION.bib ·
Download references.bib (method papers + related software) or expand below.
Full BibTeX file
% -----------------------------------------------------------------------------
% Theory and methods
% -----------------------------------------------------------------------------
@book{samson1991robot,
title = {Robot Control: The Task Function Approach},
author = {Samson, Claude and Espiau, Bernard and Le Borgne, Michel},
year = {1991},
publisher = {Oxford University Press},
}
@article{sugihara2011solvability,
title = {Solvability-unconcerned inverse kinematics by the {Levenberg--Marquardt} method},
author = {Sugihara, Tomomichi},
journal = {IEEE Transactions on Robotics},
volume = {27},
number = {5},
pages = {984--991},
year = {2011},
}
@article{sola2018micro,
title = {A micro {Lie} theory for state estimation in robotics},
author = {Sol{\`a}, Joan and Deray, Jeremie and Atchuthan, Dennis},
journal = {arXiv preprint arXiv:1812.01537},
year = {2018},
}
@misc{caron2023jacobian,
author = {Caron, St{\'e}phane},
title = {Jacobian of a kinematic task and derivatives on manifolds},
year = {2023},
howpublished = {\url{https://scaron.info/robotics/jacobian-of-a-kinematic-task-and-derivatives-on-manifolds.html}},
}
% -----------------------------------------------------------------------------
% Related software
% -----------------------------------------------------------------------------
@misc{zakka2024mink,
author = {Zakka, Kevin},
title = {mink: differential inverse kinematics for {MuJoCo}},
howpublished = {\url{https://github.com/kevinzakka/mink}},
}
@misc{caron2024pink,
author = {Caron, St{\'e}phane},
title = {Pink: {Python} inverse kinematics with {Pinocchio}},
howpublished = {\url{https://github.com/stephane-caron/pink}},
}
@misc{deepmind2025mujocowarp,
author = {{Google DeepMind}},
title = {{MuJoCo Warp}},
howpublished = {\url{https://github.com/google-deepmind/mujoco_warp}},
}
@misc{newtonphysics2025newton,
author = {{NVIDIA and Google DeepMind}},
title = {Newton: {GPU}-accelerated physics for robot learning},
howpublished = {\url{https://github.com/newton-physics/newton}},
}
Samson, Espiau, Le Borgne, 1991.
Sugihara, 2011.
Solà et al., 2018.
Caron, 2023.
Zakka, mink.
Caron, Pink.
Google DeepMind, MuJoCo Warp.
NVIDIA and Google DeepMind, Newton.