Introduction to Multimodal Reasoning for Robotics and Process Intelligence

The Multimodal Reasoning for Robotics and Process Intelligence research group is dedicated to advancing intelligent systems that seamlessly integrate diverse data modalities, such as vision, language, and sensor signals, to enable robust reasoning, perception, and decision-making for complex robotic and industrial processes. Our research combines artificial intelligence, robotics, and industrial systems engineering, focusing on the development of adaptive, reliable, and explainable autonomous systems capable of operating in dynamic and uncertain environments. By integrating state-of-the-art AI techniques with control theory, optimization, and system modeling, we aim to create solutions that are both theoretically innovative and practically impactful.

A core focus of our group is multimodal data integration. Modern industrial and robotic systems generate vast streams of heterogeneous data, including visual information, sensor measurements, and textual or language-based inputs. We develop methods that fuse these data streams, enabling autonomous agents to understand complex environments, reason about their current state, and make context-aware decisions. This multimodal approach allows our systems to operate robustly in real-world settings, where uncertainty and variability are inherent.

The research group engages in a wide range of research activities that demonstrate the diversity and applicability of our work. We develop advanced control strategies using deep learning and reinforcement learning to optimize complex biochemical and industrial processes. Our research in AI-driven decision support enhances operational efficiency, automates problem-solving, and enables robust performance in industrial settings. We also investigate the integration of advanced materials, robotics, and control systems to improve safety, efficiency, and reliability in renewable energy and large-scale industrial operations.

In addition, our work spans environmental and sustainability challenges, where we combine AI and predictive control methods to reduce emissions, monitor critical resources, and improve process efficiency. We design autonomous robotic systems for inspection and monitoring tasks, applying advanced perception, navigation, and control techniques to operate safely and cost-effectively in challenging environments such as wind turbines, subsea pipelines, and industrial machinery. Collectively, these research activities reflect our group’s ability to bridge cutting-edge AI, robotics, and control technologies with practical, real-world applications, delivering adaptive, reliable, and high-impact solutions.

Our research group strives to push the boundaries of intelligent autonomous systems by integrating multimodal data, advanced AI methods, and real-world applications. We aim to build systems that are adaptive, reliable, and impactful, with contributions spanning robotics, renewable energy, industrial automation, and sustainable technologies.