ORICF -- Open Robotics Inference and Control Framework
For roboticists deploying AI models on resource-constrained robots, ORICF provides a modular, model-agnostic platform that significantly reduces onboard compute and energy via edge offloading.
ORICF is a modular framework for composing multimodal robotic inference pipelines that supports edge offloading. On a mobile robot combining ASR, LLM, and CNN, edge deployment reduced robot-side compute utilization by up to 83.16% and estimated energy consumption by 65.8%.
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.