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Stochastic Multiple Shooting Trajectory Optimization via Sequential Local Policy Evaluation — arXiv:2608.03978

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03978
License (as archived): CC0-1.0
Archived: 2026-10-31T13:08:25.756Z
SHA-256 of the archived text: 12f872fc924d4f37f644fd3a4e49fcf9b86e04b9368e593ea86317af7e312ec9
Stable URL of this page: https://metraipolis.com/documento/dc_2bae889b34ea

How to cite

Stochastic Multiple Shooting Trajectory Optimization via Sequential Local Policy Evaluation — arXiv:2608.03978 — arXiv (en), https://arxiv.org/abs/2608.03978 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bae889b34ea (Metraipolis archive dc_2bae889b34ea, sha256 12f872fc924d4f37f644fd3a4e49fcf9b86e04b9368e593ea86317af7e312ec9)

BibTeX

@misc{dc2bae889b34ea,
  title = {Stochastic Multiple Shooting Trajectory Optimization via Sequential Local Policy Evaluation — arXiv:2608.03978},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03978},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bae889b34ea; sha256 12f872fc924d4f37f644fd3a4e49fcf9b86e04b9368e593ea86317af7e312ec9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03978. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Stochastic single shooting trajectory optimization methods such as Model Predictive Path Integral control (MPPI) have been widely adopted in robotics due to their ability to reason about probabilistic dynamics and provide solutions where model gradients are noisy, costly to evaluate, or unavailable. However, satisfaction of terminal constraints when shooting over long action sequences is often sample inefficient, requiring a large number of iterations for convergence. In this paper, we present a stochastic multiple shooting method that optimizes short control action sequences connected via local feedback policies to improve sample efficiency and convergence to a terminal set. Additionally, we show that we are able to synthesize approximate system Jacobians purely from rollouts, making the method suitable for model-based reinforcement learning with black-box dynamics. We demonstrate the algorithm has improved sample efficiency and terminal set convergence for three nonlinear, underactuated optimization problems: a classic cartpole swingup task with analytical dynamics, a cartpole swingup task with learned neural network dynamics, and a VTOL quadplane performing a high angle-of-attack, precision post-stall landing maneuver.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-05 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.03978 — 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 2026-10-31T12:55:56.570Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).