EN DE DE Menu | | Embodied AI Prof. Dr. Area Chair Embodied AI Scientific Coordinator Embodied AI Basics read more ‹ › About us Research areas Interdisciplinary research Knowledge Latest news Application Information Social networks Partner organizations Institutionally funded by Privacy Preference We need your consent before you can continue on our website. If you are under 16 and wish to give consent to optional services, you must ask your legal guardians for permission. We use cookies and other technologies on our website. Some of them are essential, while others help us to improve this website and your experience. Personal data may be processed (e.g. IP addresses), for example for personalized ads and content or ad and content measurement. You can find more information about the use of your data in our privacy policy. You can revoke or adjust your selection at any time under Settings. 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Essential (1) Show Cookie Information Hide Cookie Information External Media (7) External Media Show Cookie Information Hide Cookie Information Facebook Google Maps Instagram OpenStreetMap Twitter Vimeo YouTube Research areas Home | Research | Embodied AI Embodied Artificial Intelligence refers to AI that is embedded in physical systems, such as robots, and can interact with the surroundings. In contrast to classic Machine Learning in robotics, embodied AI encapsulates all aspects of interacting and learning in an environment: from perception, via understanding, reasoning, and planning to execution respectively manipulation. Just as human learning is based on exploration and interaction with the environment, embodied agents must improve their behavior from experience. Thus, embodied AI brings together multiple fields, such as computer vision, environment modeling, and prediction, planning, and control, reinforcement learning, physics-based simulation, and robotics. At present, AI research is highly data driven. The revolutionary breakthrough of Deep Learning consequently happened in application areas where data is readily available or can easily be generated. This makes sense in cyber areas, such as the internet, which by itself is a source of tremendous amounts of data. Looking at Europe and especially Germany, our societies’ success is strongly based on engineering and robotics and focusses heavily on manufacturing as well as logistics. As a result, there is a strong need to focus more on AI application for machines. Consequently, research on AI for machines needs a paradigm shift towards embodied AI. In the real world, intelligence should not exist in isolation; instead, it manifests itself though rich, multimodal interaction with the environment. So, an intelligent agent cannot be seen solely as a cyber component, but as an agent embodying the machinery. For such an embodied intelligent agent, its physical structure and properties, its sensor equipment, and its actuation capabilities play a critical role. In line with the AI3 paradigm, our approach to advance embodied AI is to add suitable structure to deep reinforcement learning. In this way, perception and planning of complex agent-environment interaction can be learned from limited experience. Simulation will be used to generate a digital reality enabling the agent to learn, relearn, and validate. Accordingly, embodied AI is not only of theoretical interest but of relevance to many real-world applications. Indeed, it concerns many of the application areas investigated at the Lamarr Institute, such as Natural Language Processing (NLP), manufacturing, or logistics. 15. January 2025 The advancements in artificial intelligence (AI) over the past few years have been remarkable, with various fields... Behrens, Tjark; Zurbrügg, René; Pollefeys, Marc; Bauer, Zuria; Blum, Hermann, 2025 Herraez, Daniel Casado; Kaschner, Franz; Zeller, Matthias; Muhle, Dominik; Behley, Jens; Heidingsfeld, Michael; Cremers, Daniel; Stachniss, Cyrill, 2025 Jose, Allen Isaac; Pan, Sicong; Zaenker, Tobias; Menon, Rohit; Houben, Sebastian; Bennewitz, Maren, 2025 Kruzhkov, Evgenii; Behnke, Sven, 2025 Kuang, Haofei; Pan, Yue; Zhong, Xingguang; Wiesmann, Louis; Behley, Jens; Stachnis, Cyrill, 2025 Marques, Joao Marcos Correia; Dengler, Nils; Zaenker, Tobias; Mucke, Jesper; Wang, Shenlong; Bennewitz, Maren; Hauser, Kris, 2025 Menon, Rohit; Dengler, Nils; Pan, Sicong; Chenchani, Gokul Krishna; Bennewitz, Maren, 2025 Pan, Yue; Zhong, Xingguang; Jin, Liren; Wiesmann, Louis; Popović, Marija; Behley, Jens; Stachniss, Cyrill, 2025 Villar-Corrales, Angel; Behnke, Sven, 2025 Villar-Corrales, Angel; Plepi, Gjergj; Behnke, Sven, 2025 Lamarr Institute for Machine Learning and Artificial Intelligence © 2025 | All Rights Reserved We need your consent before you can continue on our website. 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