Abu Dhabi AI-Robotics Conference

Human-Robot Symbiosis: From Human Modeling to Neurally-Driven Symbiotic Embodiment

Abstract: In developing transformative neuroengineering technologies for restoring autonomy and mobility to those with physical disabilities, a shift towards a more compatible, safe, and anticipatory human-centered approach is essential. This necessitates the integration of advanced AI and machine learning algorithms capable of leveraging high throughput sensory data (including kinematics, eye-tracking, invasive and non-invasive brain data) and human neuromechanics models (human digital twins). Rather than expecting humans to conform to and learn how to operate robotic systems, including prosthetics, robot neuroassistants, or brain-controlled humanoids, the focus is on designing systems that recognize human intent and emulate natural limb and whole-body behavior. This entails embedding models of human movement and interaction into robot learning and control algorithms, maximizing therapeutic effects and patient benefit as well as achievable autonomy. The core of this approach is the development of human digital twins driven by multi-modal sensory data, encompassing neuromechanics modeling, muscle dynamics, and control mechanisms. By learning from human physiology, robot systems can exhibit anthropomorphic traits and reflexes, achieving advanced capabilities while retaining humanlike responses. The use of intelligent control and learning algorithms enables the realization of AI-driven systems, such as prostheses, which facilitate real-time, user-in-loop interaction and functionality, such as grasping and motion compensation.

Under this new perspective, transformative technologies also advance beyond basic Brain Machine Interfaces (BMI), with the focus shifting from mere brain signal processing to neurally-driven symbiotic embodiments, aiming to decode neural signals for more natural human-like behavior in robotic systems. This includes decoding full state joint, impedance, and force policies and integrating human digital twins to enhance user embodiment. In summary, this new generation of transformative technologies paves the way for a new era of human-model-informed, symbiotic embodiments, enhancing the integration and effectiveness of AI-empowered robotic systems in various domains, particularly in healthcare and rehabilitation.

Bio: Sami Haddadin is Vice President for Research and Professor of Robotics at the Mohamed bin Zayed University of Artificial Intelligence. He has published more than 200 scientific articles and is recognized for his contributions to robotics and AI. Haddadin received his doctorate summa cum laude from RWTH Aachen University in 2011 and subsequently worked as a research assistant at the German Aerospace Center (DLR). From April 2014 to April 2018, he held the Chair of Automatic Control at the Gottfried Wilhelm Leibniz University Hannover, and from 2018 until January 2025, he was a professor and Chair of Robotics and Systems Intelligence at the Technical University of Munich (TUM). During this time he was also the founding and executive director of the Munich Institute of Robotics and Machine Intelligence (TUM MIRMI). Haddadin has been involved in various initiatives and commissions, such as the Robot Factory training program in Hanover and the Study Commission on Artificial Intelligence in the German Parliament. He has served on the EU High-Level Industrial Roundtable, Industry 2030, and the EU High-Level Expert Group on Artificial Intelligence. In 2020, he was appointed Chairman of the Bavarian AI Council. Haddadin has received several prestigious awards, including the Gottfried Wilhelm Leibniz Prize in 2019 and the Alfried Krupp Sponsorship Award for Young University Teachers in 2015. In 2017, he and his colleagues Simon Haddadin and Sven Parusel were awarded the German Future Prize for their work on affordable, flexible, and user-friendly robots. In 2021, Haddadin was elected a member of the German National Academy of Sciences Leopoldina.