Metraipolis › archive › dc_bfdd7bb69fb2

DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction — arXiv:2609.39198

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39198
License (as archived): CC0-1.0
Archived: 2026-10-07T15:11:57.335Z
SHA-256 of the archived text: 895a058c8361bda765cb042c7ce7e2c26e6f9ab2ddcd888abcaf5968d66f4ed9
Stable URL of this page: https://metraipolis.com/documento/dc_bfdd7bb69fb2

How to cite

DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction — arXiv:2609.39198 — arXiv (en), https://arxiv.org/abs/2609.39198 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bfdd7bb69fb2 (Metraipolis archive dc_bfdd7bb69fb2, sha256 895a058c8361bda765cb042c7ce7e2c26e6f9ab2ddcd888abcaf5968d66f4ed9)

BibTeX

@misc{dcbfdd7bb69fb2,
  title = {DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction — arXiv:2609.39198},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39198},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bfdd7bb69fb2; sha256 895a058c8361bda765cb042c7ce7e2c26e6f9ab2ddcd888abcaf5968d66f4ed9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39198. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

While Vision-Language-Action (VLA) models excel in static tasks, they struggle in dynamic environments where objects are in motion (e.g., conveyor belt manipulation). We identify three fundamental limitations hindering current VLAs in these scenarios: the \textbf{perception gap}, where static visual inputs lack temporal motion cues; the \textbf{latency gap}, where inference delays render actions obsolete; and the \textbf{control gap}, caused by the open-loop action chunk execution without real-time adjustment. In this work, we propose \textbf{DSDyn-VLA}, a Slow-Fast \textbf{D}ual-\textbf{S}tream \textbf{Dyn}amic manipulation framework that integrates motion-aware foresighted planning with real-time residual correction. The slow \textbf{Flow-Planner} serves as a macro-planner. By enhancing the VLA with optical flow for temporal perception and a future state awareness mechanism to preemptively offset inference latency, it produces globally consistent, motion-aware action chunks. Complementing this, the fast \textbf{Res-Refiner} employs a lightweight RL policy to inject high-frequency, closed-loop corrections into the planned action chunks based on real-time observations. In addition, we introduce \textbf{DynBench}, a MuJoCo-based benchmark for dynamic object manipulation that comprises nine tasks. Extensive experiments demonstrate that DSDyn-VLA reduces the failure rate by over 76\% compared to current SOTA method in high-latency setting on the Kinetix dynamic benchmark, while achieving about 6$\times$ the success rate of PI0.5 in real-world dynamic settings and about 5$\times$ on DynBench. We will open-source all the code and weights.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-01 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.39198 — 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 2026-10-07T14:44:47.425Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).