ORICF让机器人低耗高效运行多模态AI，支持边缘部署
ORICF -- Open Robotics Inference and Control Framework
- 用轻量YAML配置实现模块化推理流水线，无需改代码
- 边缘部署使机器人算力占用降83.16%，能耗降65.8%
- 适合需低延迟、高能效的移动机器人研发团队
人工智能进步推动了机器人感知与语言模型的发展，但其部署仍计算开销大，导致延迟高、能耗高。本文提出开放机器人推理与控制框架（ORICF），一个模块化、声明式且模型无关的平台，用于构建多模态机器人推理流程。ORICF集成输入输出适配器、可插拔推理后端和后处理逻辑，通过轻量级YAML配置即可更改模型、硬件目标和数据通道而无需修改代码。该框架支持边缘卸载，即在附近外部计算机上执行推理而非在机器人本地。在搭载ROS2的移动机器人上评估：通过结合自动语音识别（ASR）、大语言模型（LLM）和卷积神经网络（CNN）检测器，实现对摄像头画面中人物的语音查询应答。相比本地执行，基于ORICF的边缘部署将机器人侧计算利用率降低最高达83.16%，预计能耗降低65.8%，同时保持模块化与可复现性。
原文摘要 · Abstract (English)
Recent advances in artificial intelligence (AI) have enabled effective perception and language models for robots, but their deployment remains computationally expensive, increasing latency and energy use. This work presents the Open Robotics Inference and Control Framework (ORICF), a modular, declarative, and model-agnostic platform for composing multimodal robotic inference pipelines. ORICF integrates input/output (I/O) adapters, pluggable inference back ends, and post-processing logic, while lightweight YAML specifications allow models, hardware targets, and data channels to be changed without code modification. The framework also supports edge offloading, i.e., executing inference on nearby external computers instead of onboard the robot. ORICF is evaluated on a mobile robot that answers spoken queries about people detected in its camera stream by combining automatic speech recognition (ASR), a large language model (LLM), and a convolutional neural network (CNN) detector through Robot Operating System 2 (ROS2). Compared with onboard execution, ORICF-based edge deployment reduces robot-side compute utilization by up to 83.16% and estimated energy consumption by 65.8%, while preserving modularity and reproducibility.
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