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Completed TRL 2 (started at 2, targeting 4)
REACH (Responsive Embedded Assistant for Command Handling) is a modular architecture developed to enable natural language-driven responsive autonomy in edge computing environments. Unlike traditional responsive systems that rely on deterministic planning and centralized control, REACH operates entirely at the edge—near the data source—without dependence on cloud-based resources. It integrates large language models (LLMs), computer vision, and responsive control into a distributed framework that allows for flexible, low-latency task execution. This approach supports mission-critical applications such as in-space servicing, assembly, and human-robot teaming, where adaptability and earth independence are essential.
The system is composed of four interconnected modules: a responsive arm with onboard compute, a vision-based perception module, a natural language translation interface, and a communication module built on ROS2. REACH translates spoken or typed commands into structured responsive procedures using locally hosted LLMs and speech recognition tools. Object detection and localization are performed using a single RGB camera and ArUco markers, enabling consistent spatial awareness without extensive calibration. The architecture’s modularity allows components like kinematics solvers and perception systems to be incrementally upgraded, providing a scalable pathway toward advanced autonomous responsive for future space missions.
This project initially sought to explore the use of a Large Language Model (LLM) with access to knowledge of a specific system for the purpose of generating specific procedures based on detected faults. Due to difficulties in obtaining specific datasets to test and train the AI Procedure Generator, this project was pivoted into the Responsive Embedded Assistant for Command Handling (REACH) project. Benefits of this project include: distributed intelligence in edge-computing devices, natural language translation between LLM applications and hardware commanding, computer vision model in-house training, and advancements in solving reverse kinematics equations.
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