机器人AI模型终于能跑在廉价芯片上
现在的机器人AI模型（比如能看、能说、能动的VLA模型）部署起来极其麻烦：每个模型要不同的Python环境、不同的硬件驱动，还得写一堆胶水代码。这篇论文搞了个C++运行时Embodied.cpp，把各种模型的共同执行路径抽成5个模块（输入适配、序列构建、骨干执行、头部插件、部署适配），让同一个模型能无缝跑在树莓派、Jetson、甚至不同机器人上。实测两个VLA模型任务成功率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，但意味着未来机器人AI不再需要昂贵的专用硬件，普通边缘设备也能跑复杂模型。
📄 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.