[2607.02501] Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots

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Computer Science > Robotics

arXiv:2607.02501 (cs)

[Submitted on 2 Jul 2026]

Title:Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots

Authors:Ling Xu, Chuyu Han, Borui Li, Hao Wu, Shiqi Jiang, Ting Cao, Chuanyou Li, Sheng Zhong, Shuai Wang<br>View a PDF of the paper titled Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots, by Ling Xu and 8 other authors

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Abstract:Embodied AI models now span vision-language-action (VLA) models and world-action models (WAMs), but practical deployment remains fragmented across model-specific Python stacks, backend assumptions, and robot-side glue code, especially on heterogeneous edge devices. Existing inference runtimes are designed mainly for request-response serving and therefore do not satisfy the runtime contract of embodied deployment: multi-rate execution inside closed-loop control, latency-first batch-1 inference on heterogeneous hardware, and extensible embodied interfaces beyond fixed token I/O. We present this http URL, a portable C++ inference runtime for embodied models. Based on an architectural analysis of representative VLA models and WAMs, this http URL captures a shared execution path and organizes it into five layers: input adapters, sequence builders, backbone execution, head plugins, and deployment adapters. The runtime provides modular multi-rate execution, latency-first fused inference, and extensible operator and I/O support, enabling deployment across heterogeneous devices, robots, and simulators through one backend abstraction. We evaluate this http URL on two VLA models, HY-VLA and pi0.5, and on a preliminary WAM benchmark using a LingBot-VA Transformer block. The VLA deployments achieve successful closed-loop execution with 100.0% and 91.0% task success rates, respectively. The WAM benchmark reduces block memory from 312.2 MiB to 88.1 MiB. These results show that this http URL improves deployment efficiency while preserving high accuracy across diverse embodied model architectures.

Comments:<br>12 pages, 2 figures, Project website: this https URL

Subjects:

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Operating Systems (cs.OS)

Cite as:<br>arXiv:2607.02501 [cs.RO]

(or<br>arXiv:2607.02501v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.02501

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Borui Li [view email]<br>[v1]<br>Thu, 2 Jul 2026 17:58:28 UTC (1,625 KB)

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