Leveraging Foundation Models in Robotics: Transforming Task Planning and Contextual Execution

Abstract

This paper explores integrating foundation models, particularly Transformers, into robotic systems to address challenges in task planning and contextual execution. Current robotic methodologies often struggle with adaptability and real-time decision-making in dynamic environments, limiting their effectiveness in complex tasks. Foundation models, known for their success in natural language processing and computer vision, offer significant potential to enhance robotic performance through improved contextual awareness and adaptability. The paper proposes a conceptual framework for incorporating these models into robotic architectures, detailing the necessary adaptations to model architecture, training techniques, and real-time sensor data integration. It also discusses expected outcomes, including increased precision, adaptability to new environments, and handling complex tasks. Finally, the paper identifies key areas for future research, such as exploring alternative foundation models, advancing training methodologies, and developing new evaluation metrics for robotic systems. This review underscores the transformative potential of foundation models in robotics and calls for continued innovation to realize their benefits fully.