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Retiming Robot Demonstrations with a Learned Speed Prior — arXiv:2610.32676

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.32676
License (as archived): CC0-1.0
Archived: 2026-10-26T19:50:44.931Z
SHA-256 of the archived text: 7d3ca6d4515518ba7b4cbf6810a81d7dbe3cdb48168cac51738a7c86f6863739
Stable URL of this page: https://metraipolis.com/documento/dc_bb52ccde9baa

How to cite

Retiming Robot Demonstrations with a Learned Speed Prior — arXiv:2610.32676 — arXiv (en), https://arxiv.org/abs/2610.32676 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb52ccde9baa (Metraipolis archive dc_bb52ccde9baa, sha256 7d3ca6d4515518ba7b4cbf6810a81d7dbe3cdb48168cac51738a7c86f6863739)

BibTeX

@misc{dcbb52ccde9baa,
  title = {Retiming Robot Demonstrations with a Learned Speed Prior — arXiv:2610.32676},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.32676},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb52ccde9baa; sha256 7d3ca6d4515518ba7b4cbf6810a81d7dbe3cdb48168cac51738a7c86f6863739},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.32676. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The pace of a robot demonstration is often determined by the teleoperator rather than by the task itself. As a result, the same motion can appear at widely different speeds across demonstrations, and policies trained on such data may inherit operator-specific timing along with task behavior. We propose to learn a speed prior directly from large-scale demonstration data. Given a task instruction, a motion path and the images along it, it predicts a distribution over execution speed along the path, from which a chosen quantile defines a consistent clock for retiming each demonstration. Existing methods derive the acceleration signal from the very data they accelerate, so each new task costs another fit, annotation, or pair of predefined rates. A single trained speed prior instead covers every task in a corpus. We fit a speed prior on both robot teleoperation data and handheld demonstrations in which a human arm, rather than a robot arm, moves the gripper, and use it to retime robot demonstrations to the handheld pace before fine-tuning a vision-language-action policy. For execution, we pair the policy with a real-time optimal-control layer that tracks predicted action chunks under robot constraints using the predicted pace. On 120 RoboLab tasks, policies fine-tuned on our learned clocks succeed more often and move with up to half the jerk compared with policies fine-tuned on the original demonstration timing and uniformly accelerated to the same speed. On a real robot, our policy stacks cups in 3.50s, 1.72$\times$ faster than the default policy's 6.02s while succeeding as often, in 29 of 30 trials. Videos and further material are available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.32676 — 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 2026-10-26T19:45:33.329Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).