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Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model — arXiv:2609.08958

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08958
License (as archived): CC0-1.0
Archived: 2026-10-16T15:31:28.611Z
SHA-256 of the archived text: 4f813b01291dc848ba52fe2cd70bdcc986509f1056a47e006b96a25a2e3e2591
Stable URL of this page: https://metraipolis.com/documento/dc_e2faeee319ba

How to cite

Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model — arXiv:2609.08958 — arXiv (en), https://arxiv.org/abs/2609.08958 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e2faeee319ba (Metraipolis archive dc_e2faeee319ba, sha256 4f813b01291dc848ba52fe2cd70bdcc986509f1056a47e006b96a25a2e3e2591)

BibTeX

@misc{dce2faeee319ba,
  title = {Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model — arXiv:2609.08958},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08958},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e2faeee319ba; sha256 4f813b01291dc848ba52fe2cd70bdcc986509f1056a47e006b96a25a2e3e2591},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08958. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich dynamics and partial observability. This work presents a model predictive path integral (MPPI) controller for a three-bar tensegrity robot driven by a learned graph neural network (GNN) dynamics model. This work first extends prior GNN-based models with a differentiable contact detection module. The extension allows the dynamics model to reason over non-horizontal planar terrains, obstacles, as well as self-collisions. Then, the learned dynamics model and the MPPI controller operate in a closed data-collection loop, iteratively improving model accuracy and control performance. This work further introduces a hybrid MPPI strategy that combines MPPI with turning motion primitives to improve maneuverability. Experiments are performed in MuJoCo across five navigation tasks, which include, wall obstacles, inclines, narrow corridors, low-clearance structures, and a composite 3D obstacle course. The experiments demonstrate that the hybrid MPPI controller operating over the learned GNN dynamics model improves predictive accuracy over a flat-ground baseline model and achieves superior navigation performance compared to $A^*$-based re-planning and MPPI-only variants. Results show that the contact-aware learned dynamics combined with the sampling-based model predictive control enable robust tensegrity navigation in complex, contact-rich environments.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.08958 — 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 2026-10-16T15:11:47.658Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).