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AC-SMPC: Differentiable Actor--Critic MPC with State Constraints for Safe and Fast Robot Navigation — arXiv:2610.41477

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41477
License (as archived): CC0-1.0
Archived: 2026-11-03T08:22:20.251Z
SHA-256 of the archived text: 9e94aa7825c4a7e8a599cfb79b76221b93362b47912be0119a2347d1dbbca6b5
Stable URL of this page: https://metraipolis.com/documento/dc_bf10f2abc430

How to cite

AC-SMPC: Differentiable Actor--Critic MPC with State Constraints for Safe and Fast Robot Navigation — arXiv:2610.41477 — arXiv (en), https://arxiv.org/abs/2610.41477 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bf10f2abc430 (Metraipolis archive dc_bf10f2abc430, sha256 9e94aa7825c4a7e8a599cfb79b76221b93362b47912be0119a2347d1dbbca6b5)

BibTeX

@misc{dcbf10f2abc430,
  title = {AC-SMPC: Differentiable Actor--Critic MPC with State Constraints for Safe and Fast Robot Navigation — arXiv:2610.41477},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41477},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bf10f2abc430; sha256 9e94aa7825c4a7e8a599cfb79b76221b93362b47912be0119a2347d1dbbca6b5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41477. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Achieving safe navigation in confined dynamic environments remains a challenging problem, as robots must balance navigation efficiency with safety. Existing actor--critic MPC architectures typically employ differentiable MPC layers, but this way cannot readily accommodate explicit state constraints, making it intractable to involve collision avoidance within learning-based planning. To address this issue, we propose an actor--critic model predictive control framework with state constraints. We first use vertices to represent the robot and obstacles, and half-spaces to represent the workspace. Based on these, the nonlinear robot dynamics is locally linearized and the hyperplane-based collision avoidance constraints are convexified into the differentiable MPC layer, and then the actor exploits closed-loop feedback to adjust the MPC objective parameters that shape local motion behavior, while the critic evaluates long-term task performance. We further relax the convexified constraints with bounded and penalized slack variables to form an elastic quadratic program, accommodating cases where the local constraint approximations cannot be satisfied simultaneously. Simulation tests and experiments, involving dynamic collision avoidance for multi-robot in narrow cases, illustrate the advantages of the proposed framework in adaptively adjusting the learned policy and improving navigation efficiency over existing approaches while maintaining the motion safety.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.41477 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T08:06:53.829Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).