Sari la conținut

Română · beta. Traducerea a fost făcută automat și este încă în curs de revizuire. Citește pagina în engleză

perspectiva aiqbenchProduse AI

analiza aiqbench

ORCESTRA represents a significant advancement in robotics by integrating vision-language models into mixed-reality environments to enable no-code programming of heterogeneous robot embodiments. This system leverages AI for metric grounding and embodiment-aware validation, effectively using digital twins as a safety layer before physical deployment. The core innovation lies in how large multimodal models translate natural language commands directly into structured execution plans for diverse robotic hardware.
Sursă care acoperăarXiv
Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.

ORCESTRA: VLM-driven Visual Robot programming in Mixed Reality

arXiv1 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.00775v1 Announce Type: new Abstract: ORCESTRA is a mixed-reality system for programming robot digital twins through no-code waypoint teaching and language-guided control. In a passthrough mixed-reality workspace, users place robot twins on real surfaces, teach trajectories, save robot-relative episodes, or issue spoken/typed commands that a vision-language model converts into structured digital-twin plans. Both interaction modes share a backend for metric grounding, embodiment-aware validation, preview, confirmation, and digital-twin execution. The system supports heterogeneous robot embodiments, including fixed-base manipulators, a mobile base, and a humanoid robot, demonstrating MR validation as a safety layer for language-guided robot programming before physical deployment.

Abstract:ORCESTRA is a mixed-reality system for programming robot digital twins through no-code waypoint teaching and language-guided control. In a passthrough mixed-reality workspace, users place robot twins on real surfaces, teach trajectories, save robot-relative episodes, or issue spoken/typed commands that a vision-language model converts into structured digital-twin plans. Both interaction modes share a backend for metric grounding, embodiment-aware validation, preview, confirmation, and digital-twin execution. The system supports heterogeneous robot embodiments, including fixed-base manipulators, a mobile base, and a humanoid robot, demonstrating MR validation as a safety layer for language-guided robot programming before physical deployment.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)

I.2.9; H.5.2; I.2.7; I.3.7

arXiv:2608.00775 [cs.RO]

(or arXiv:2608.00775v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.00775 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Text integral sub licența deschisă a publicației · 199 cuvinte

Subiecte conexe