Report Abuse

Basic Information

RAI is a flexible, vendor-agnostic framework designed to develop and deploy embodied AI capabilities on robots. It provides infrastructure for building multi-agent systems that enable natural human-robot interaction, multimodal perception and action, and scenario-driven behaviors. The repository bundles core components, tooling and demos to connect AI models, sensors and simulators to real or virtual robots. It targets robotics developers, researchers and integrators who want to add generative AI features, speech interfaces, perception modules and navigation support into robotic platforms. Documentation, setup guides and community resources are provided to help teams configure RAI for a specific robot embodiment, run simulation demos, and evaluate agents with benchmarking tools. The project emphasizes modularity so users can adopt individual packages such as ASR, TTS, simulation connectors or benchmark suites while integrating into an existing robotics stack.

Links

Categorization

App Details

Features
RAI organizes functionality into focused packages and ready-to-run demos. Core modules listed include a multi-agent core for coordination and human-robot interaction, a whoami tool for extracting robot embodiment information from documentation and URDFs, ASR and TTS packages for speech I/O, a simulation connector to link agents with simulators, and a benchmarking suite to test agents and models. Additional pieces include openset detection tools, integration with a navigation system named NoMaD, and a planned finetuning package for adapting LLMs to embodied data. The repo provides simulation demos showcasing mission planning, manipulation with grounding models, and mobile robot navigation. Documentation, contribution guides and community channels support adoption and development.
Use Cases
RAI helps teams accelerate development of intelligent robotic behaviors by providing reusable agent infrastructure and modality-specific tools. It simplifies adding conversational and perceptual capabilities through ASR and TTS components and supports perception and openness via openset detection. The whoami tool reduces effort to model a robot"s embodiment and sensors for downstream tasks. Simulation connectors and demo applications let developers validate behaviors in virtual environments before deploying to hardware. The benchmarking suite offers repeatable evaluation of agents, models and simulators. Integration with navigation tooling and examples for manipulation and autonomous operation illustrate end-to-end workflows. Documentation, community discussion and a contribution process make it practical for research groups and engineering teams to extend, benchmark and deploy embodied AI solutions.

Please fill the required fields*