Learning Multi-Robot Coordination with Invariant Consensus Stabilization

Hang Yin, Christos Verginis, Danica Kragic
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1981-1994, 2026.

Abstract

Coordinating multi-robot systems for highly dexterous tasks is challenging due to the complexity of inducing desired interactions among robots with high-dimensional dynamics. This paper introduces a learning-based multi-robot control algorithm that generates complex trajectories for executing such tasks. In particular, we design a controller that achieves multi-robot consensus; unlike standard consensus protocols, the controller is parametrized by neural networks that are derived from convex potentials and represent diffeomorphic functions of the robots’ relative states. The algorithm trains the neural networks to learn consensus policies that enable coordinated, high-precision multi-robot behaviors. A key feature of our approach is translation invariance, which ensures generalization to untrained state spaces. We prove the theoretical correctness of the proposed algorithm for an arbitrary number of robots and validate its effectiveness in two dynamic tasks, namely cooperative object transportation and forceful peg insertion. The results show that the proposed controller and policy learning significantly outperform baseline methods in terms of learning efficiency and generalization under untrained task configurations,

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-yin26a, title = {Learning Multi-Robot Coordination with Invariant Consensus Stabilization}, author = {Yin, Hang and Verginis, Christos and Kragic, Danica}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1981--1994}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/yin26a/yin26a.pdf}, url = {https://proceedings.mlr.press/v331/yin26a.html}, abstract = {Coordinating multi-robot systems for highly dexterous tasks is challenging due to the complexity of inducing desired interactions among robots with high-dimensional dynamics. This paper introduces a learning-based multi-robot control algorithm that generates complex trajectories for executing such tasks. In particular, we design a controller that achieves multi-robot consensus; unlike standard consensus protocols, the controller is parametrized by neural networks that are derived from convex potentials and represent diffeomorphic functions of the robots’ relative states. The algorithm trains the neural networks to learn consensus policies that enable coordinated, high-precision multi-robot behaviors. A key feature of our approach is translation invariance, which ensures generalization to untrained state spaces. We prove the theoretical correctness of the proposed algorithm for an arbitrary number of robots and validate its effectiveness in two dynamic tasks, namely cooperative object transportation and forceful peg insertion. The results show that the proposed controller and policy learning significantly outperform baseline methods in terms of learning efficiency and generalization under untrained task configurations,} }
Endnote
%0 Conference Paper %T Learning Multi-Robot Coordination with Invariant Consensus Stabilization %A Hang Yin %A Christos Verginis %A Danica Kragic %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-yin26a %I PMLR %P 1981--1994 %U https://proceedings.mlr.press/v331/yin26a.html %V 331 %X Coordinating multi-robot systems for highly dexterous tasks is challenging due to the complexity of inducing desired interactions among robots with high-dimensional dynamics. This paper introduces a learning-based multi-robot control algorithm that generates complex trajectories for executing such tasks. In particular, we design a controller that achieves multi-robot consensus; unlike standard consensus protocols, the controller is parametrized by neural networks that are derived from convex potentials and represent diffeomorphic functions of the robots’ relative states. The algorithm trains the neural networks to learn consensus policies that enable coordinated, high-precision multi-robot behaviors. A key feature of our approach is translation invariance, which ensures generalization to untrained state spaces. We prove the theoretical correctness of the proposed algorithm for an arbitrary number of robots and validate its effectiveness in two dynamic tasks, namely cooperative object transportation and forceful peg insertion. The results show that the proposed controller and policy learning significantly outperform baseline methods in terms of learning efficiency and generalization under untrained task configurations,
APA
Yin, H., Verginis, C. & Kragic, D.. (2026). Learning Multi-Robot Coordination with Invariant Consensus Stabilization. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1981-1994 Available from https://proceedings.mlr.press/v331/yin26a.html.

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