让机器人AI模型跑在任意硬件上
机器人AI模型（比如能看、能说、能动的VLA模型）现在越来越强，但部署到真实机器人上却极其麻烦：每个模型要不同的Python框架、不同的硬件后端，还得写一堆胶水代码。这篇论文做了一个叫Embodied.cpp的C++运行时，把各种模型的共同执行路径抽象成5个模块（输入适配、序列构建、骨干网络、头部插件、部署适配），让同一个模型能无缝跑在不同机器人、不同芯片上。实测两个VLA模型任务成功率分别达100%和91%，一个世界模型的内存占用从312MB降到88MB。它不是你明天就能用的工具，但它是让机器人AI从实验室走向真实世界的关键一步——以后机器人开发者不用再为每个模型重写部署代码。
📄 原文摘要(英文)
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 Embodied.cpp, a portable C++ inference runtime for embodied models. Based on an architectural analysis of representative VLA models and WAMs, Embodied.cpp 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 Embodied.cpp 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 Embodied.cpp improves deployment efficiency while preserving high accuracy across diverse embodied model architectures.