ORICF -- Open Robotics Inference and Control Framework

TL;DR

ORICF is a modular platform that enables flexible, energy-efficient multimodal inference pipelines for robots, supporting edge offloading and easy configuration without code changes.

Contribution

It introduces ORICF, a declarative, model-agnostic framework for robotic inference that simplifies deployment and reduces energy consumption through edge offloading.

Findings

Edge deployment with ORICF reduces robot compute by 83.16%.

Energy consumption decreases by 65.8% with ORICF.

Framework maintains modularity and reproducibility.

Abstract

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.