THE EVOLUTION OF ROBOTIC SENSING

Abstract

Generative AI is revolutionizing robotic sensing by significantly enhancing the adaptability and efficiency of robots in dynamic, unstructured environments. Traditional camera-based robotic sensing systems, particularly those based on feature extraction methods like SIFT and SURF, often struggle in complex real-world conditions, such as varying lighting or object occlusion, leading to high error rates. Generative AI, utilizing multimodal transformer architectures, offers a solution by processing multiple data sources such as RGB images, depth maps, and proprioceptive data, allowing robots to make context-aware decisions. These models improve the accuracy of tasks such as robotic manipulation, object detection, and real-time decision-making in unpredictable environments. Despite these advancements, challenges such as high computational demands, privacy concerns, and domain adaptation persist. The integration of generative AI into robotics promises to enhance industries like manufacturing, healthcare, and logistics, driving innovation and improving operational efficiency. As generative AI continues to evolve, it will shape the future of collaborative robotics, making robots more intelligent and adaptable partners in various sectors.