Gaze2Instruct (G2I): Towards a More Inclusive Language-Conditioned Robotic Assistance for Severe Speech and Motor Impairments

People with severe speech and motor impairment (SSMI) often require assistive technologies to control their environment, including robots. Current eye-gaze-controlled robotic systems, however, are limited in scope, focusing on specific tasks or requiring structured command sequences. In this work, we introduce Gaze2Instruct (G2I), a novel approach for predicting the intentions of people with SSMI. G2I leverages eye-gaze data and visual input to automatically generate natural language instructions, which can then be interpreted by existing language-conditioned robotics. By translating eye-gaze into versatile language commands, we enable intuitive interaction with assistive robots for individuals with SSMI, allowing for unstructured, real-time task execution without predefined grammar or task-specific solutions, leveraging the power of segmentation models and Multimodal Large Language Models (MLLM). Through a series of experiments, we demonstrate the effectiveness of our system in generating accurate and meaningful instructions, reducing cognitive load, and improving the ease of interaction for users with SSMI.