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Goal-Oriented Communications for Physical AI: Design and Testbed — arXiv:2609.15895

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.15895
License (as archived): CC0-1.0
Archived: 2026-10-16T02:14:56.393Z
SHA-256 of the archived text: ca98d523dc7bd61df4a5c7e1e7289183d0c01e31b0a37347f19f7aa72be13e10
Stable URL of this page: https://metraipolis.com/documento/dc_fb2a366d4247

How to cite

Goal-Oriented Communications for Physical AI: Design and Testbed — arXiv:2609.15895 — arXiv (en), https://arxiv.org/abs/2609.15895 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fb2a366d4247 (Metraipolis archive dc_fb2a366d4247, sha256 ca98d523dc7bd61df4a5c7e1e7289183d0c01e31b0a37347f19f7aa72be13e10)

BibTeX

@misc{dcfb2a366d4247,
  title = {Goal-Oriented Communications for Physical AI: Design and Testbed — arXiv:2609.15895},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.15895},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fb2a366d4247; sha256 ca98d523dc7bd61df4a5c7e1e7289183d0c01e31b0a37347f19f7aa72be13e10},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.15895. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Physical AI relies on frequently-updated, latency-sensitive video stream to perceive, reason, and interact with the physical world, resulting in strict latency requirements with much higher data volumes that existing 5G networks cannot support. Goal-oriented communication (GoC) offers as a promising approach to solve this challenge by transmitting only task-relevant semantic representations. However, existing GoC frameworks were mainly evaluated in the simulations while their effectiveness has never been validated in a practical deployment of physical AI application. In this work, we develop an end-to-end GoC testbed for Physical AI, which connects a PiPER robot arm equipped with an RGB-D camera and a 5G modem to an NVIDIA Jetson AGX Orin edge server through a 5G OpenAirInterface network. We propose and implement three GoC frameworks that transmit 3D bounding boxes, 2D scene graphs, and 3D scene graphs, as three types of semantic representations, respectively. They share the common functional modules designed for closed-loop Physical AI applications, including semantic extraction, full stack 5G transmission, language model inference, digital twin validation, and robotic control. Extensive experiments on our testbed show that our GoC frameworks reduce the task completion time by up to 52.6% and improve task success probability by up to 45%, compared to the traditional framework that periodically transmits the raw image data. These results validate the practical effectiveness of our GoC framework and pave the way for efficient and reliable Physical AI applications over future 6G networks. Project website: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-15 in cs.RO).
Categorie arXiv: Robotics (cs.RO) ; Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2609.15895 — 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 2026-10-16T01:52:32.397Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).