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Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control — arXiv:2608.19443

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19443
License (as archived): CC0-1.0
Archived: 2026-10-18T13:40:16.384Z
SHA-256 of the archived text: 27e5bc2f09a76fbc6278c395538d2547ec8847c62c6933f645f68777fbdb43fb
Stable URL of this page: https://metraipolis.com/documento/dc_a6eefb09efd2

How to cite

Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control — arXiv:2608.19443 — arXiv (en), https://arxiv.org/abs/2608.19443 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a6eefb09efd2 (Metraipolis archive dc_a6eefb09efd2, sha256 27e5bc2f09a76fbc6278c395538d2547ec8847c62c6933f645f68777fbdb43fb)

BibTeX

@misc{dca6eefb09efd2,
  title = {Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control — arXiv:2608.19443},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19443},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a6eefb09efd2; sha256 27e5bc2f09a76fbc6278c395538d2547ec8847c62c6933f645f68777fbdb43fb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19443. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Thanks to its parallelizability and flexibility, sampling-based Model Predictive Control (MPC) has become widely popular for controlling real-world robotic systems. However, for high-dimensional and open-loop unstable dynamical systems, the required number of samples to improve the control sequence will grow exponentially with the horizon, leading to poor sample efficiency and numerical instability. This paper investigates the instability of shooting methods in sampling-based MPC and shows that the optimal sampling proposal distribution can be realized by sampling with an optimized feedback policy. We refer to this algorithm as Feedback Sampling MPC (FS-MPC). FS-MPC involves a hybrid sampling design which balances local and global search based on the system stability and the available computation budget. Our theoretical analysis shows that our hybrid sampling approach achieves faster convergence than standard MPPI and better optimality than standard feedback sampling. Empirically, in diverse contact-rich control tasks like humanoid loco-manipulation and dexterous manipulation, we show that FS-MPC successfully tackles dynamically unstable tasks where standard sample-based approaches struggle, and strictly outperforms feedback policies alone. Finally, we validate our method on humanoid robot locomotion and manipulation tasks in the real world.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-21 in eess.SY).
Categorie arXiv: Robotics (cs.RO) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2608.19443 — 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 2026-10-18T13:38:45.712Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).