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CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation — arXiv:2610.21349

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21349
License (as archived): CC0-1.0
Archived: 2026-10-16T10:39:53.363Z
SHA-256 of the archived text: 44453b2fc32e4ac983c279c95e8b41fc6629a43900870ea3778f69d06e31985c
Stable URL of this page: https://metraipolis.com/documento/dc_bc61466f91c2

How to cite

CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation — arXiv:2610.21349 — arXiv (en), https://arxiv.org/abs/2610.21349 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bc61466f91c2 (Metraipolis archive dc_bc61466f91c2, sha256 44453b2fc32e4ac983c279c95e8b41fc6629a43900870ea3778f69d06e31985c)

BibTeX

@misc{dcbc61466f91c2,
  title = {CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation — arXiv:2610.21349},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21349},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bc61466f91c2; sha256 44453b2fc32e4ac983c279c95e8b41fc6629a43900870ea3778f69d06e31985c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21349. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Efficient synthesis of contact-rich manipulation trajectories is essential for downstream robot learning. Gradient-based planning offers a promising approach, but jointly optimizing motion and contact decisions remains challenging. Meanwhile, many existing planners run on CPUs with limited throughput. We present CQDC-Warp, a GPU-accelerated differentiable quasi-dynamic simulator for batched model predictive control (MPC). We use it to study contact-guided trajectory optimization, in which intended robot--object contacts are specified before planning and encouraged through soft costs, while the simulated dynamics determine contact geometry and frictional response without a prescribed contact-mode schedule. Across $17{,}408$ trials on the $17$-object Allegro in-hand rotation benchmark, our framework achieves $79.8\%$ success, compared with $73.7\%$ for CFree and $65.2\%$ for IMPACT, and reduces mean trajectory-generation time by factors of $27.0$ and $50.2$, respectively. On a single NVIDIA RTX 4090 with $2{,}048$ parallel worlds, forward and backward throughput reach $2.06\times10^5$ and $2.25\times10^5$ world-steps per second, respectively. Further experiments cover manipulation with LEAP and Shadow hands, environmental contact exploitation, bimanual manipulation, whole-body control, and real-world trajectory replay on a Sharpa hand.

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/2610.21349 — 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 2026-10-16T08:52:17.629Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).