💡 The paper introduces Embodied.cpp, a portable C++ runtime that enables efficient deployment of embodied AI models across heterogeneous edge devices. The problem addressed is the fragmentation of embodied AI model deployment, which is currently limited by model-specific Python stacks, backend assumptions, and robot-side glue code. This makes it difficult to deploy these models on various devices, especially on heterogeneous edge devices.

The authors propose Embodied.cpp as a solution, which is based on an architectural analysis of representative vision-language-action and world-action models. The runtime is organized into five layers: input adapters, sequence builders, backbone execution, head plugins, and deployment adapters. This modular design provides multi-rate execution, latency-first fused inference, and extensible operator and I/O support, allowing for deployment across diverse devices, robots, and simulators through a single backend abstraction.

The results show that Embodied.cpp achieves successful closed-loop execution with high task success rates on two vision-language-action models, and reduces block memory usage on a preliminary world-action model benchmark. Specifically, the VLA deployments achieve 100.0% and 91.0% task success rates, while the WAM benchmark reduces block memory from 312.2 MiB to 88.1 MiB. These results demonstrate that Embodied.cpp improves deployment efficiency while preserving high accuracy across diverse embodied model architectures. Overall, the paper contributes a portable and efficient runtime for embodied AI models, enabling their deployment on a wide range of devices and robots.