Embodied.cpp: Portable Inference Runtime for Vision-Language-Action Models on Edge DevicesEmbodied.cpp：异构边缘设备上的嵌入式AI推理运行时
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Researchers presented Embodied.cpp, a C++ inference runtime designed to enable efficient deployment of vision-language-action (VLA) and world-action models (WAMs) on heterogeneous edge devices and robots.
The runtime organizes execution into five modular layers (input adapters, sequence builders, backbone execution, head plugins, deployment adapters) to solve fragmentation across model-specific Python stacks, supporting multi-rate execution and latency-optimized batch-1 inference.
The infrastructure addresses closed-loop control requirements with real-time responsiveness for embodied AI deployment, a pattern critical to autonomous vehicle perception-action loops.