Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
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 Embod
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- Linked via arxiv authorDingling Xu →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorChuyu Han →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorBorui Li →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorHao Wu →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorShiqi Jiang →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorTing Cao →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorChuanyou Li →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorSheng Zhong →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
- Linked via arxiv authorShuai Wang →
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots