Speakers

Kyrre Glette

Bio: Kyrre Glette received the Ph.D. degree in computer science from the University of Oslo, Norway, in 2008. He is currently an Associate Professor with the Robotics and Intelligent Systems Group (ROBIN), Department of Informatics, and a PI at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo. He has experience in artificial intelligence and adaptive systems, digital design, rapid prototyping, music technology, and robotics. His current research interests include algorithms for automatic adaptation and design of behaviors and shapes for robotic systems, transferring behaviors between simulation and reality, and robotic collectives.

Tentative talk title: Evolutionary design of morphology and control for simulated and real-world robots

Sami Haddadin

Bio: Sami Haddadin is Director of the Munich Institute of Robotics and Machine Intelligence. His research covers robotics, artificial intelligence and neuroscience with a focus on human-machine interaction, humanoid robots, embodied AI and robot learning, nonlinear control, collective intelligence and human-robot symbiosis. His pioneering work on the creation of cooperative robot assistants, brain controlled robots and neuroprostheses, robot safety and optimal AI is considered groundbreaking. In addition, many of his robots have been commercialized and established as a global standard. From 2014 to 2018, Prof. Haddadin held the position of Chair of Automatic Control at Gottfried Wilhelm Leibniz Universität Hannover. Prior to that, he served in various roles as a research associate at the German Aerospace Center (DLR). He holds degrees in Electrical Engineering, Computer Science, and Technology Management (TUM/ LMU) and earned his doctorate with high distinction from RWTH Aachen. He was a member of the High-Level Expert Group on AI for the European Commission and Chairman of the Bavarian AI Council.

Tentative talk title: Cooperative Human-AI Robot Design and Synthesis

Kuang-Huei Lee

Bio: Kuang-Huei Lee is a Staff Research Scientist at Google DeepMind in San Francisco. His research interests center around creating general cognitive agents in both physical and virtual worlds, and his current research spans deep generative models, reasoning, planning, reinforcement learning, and robotics. Prior to joining Google in 2019, Kuang-Huei spent 3 years at Microsoft. He received his graduate degree in Computer Science from Carnegie Mellon University, and his undergraduate degree in Mechanical Engineering from National Taiwan University. His research has been widely published, appearing in venues such as NeurIPS, ICML, ICLR, RSS, IROS, CVPR, ECCV, and EMNLP.

Tentative talk title: Embodied AI Generalization Strategies

Mariano Phielipp

Bio: Dr. Mariano Phielipp is a scientist and engineer specializing in Deep Learning, Reinforcement Learning, and AI, with expertise in robotic learning, computer vision, natural language processing, and decision-making systems. At Intel’s AI Labs, he led collaborations with the academic community and business groups to develop cutting-edge learning systems for AI in Robotics, AI for Science, and combinatorial optimization for EDA, setting new benchmarks in efficiency and performance. His 18-year tenure at Intel included roles from software engineer to Principal Engineer, with significant contributions in AI R&D and Deep Reinforcement Learning.

Mariano holds a PhD in Computer Science from Arizona State University, where he also earned his Master’s degree. His research has been featured at premier conferences such as NeurIPS, ICML, ICLR, AAAI, and CoRL, and he has delivered keynote addresses on advancements in

robotics and AI. He has contributed to numerous publications, patents, and committees, and has received recognition for his impact on robotics.

Throughout his career, Mariano has collaborated with renowned researchers from UC Berkeley, the University of Toronto, MILA, and ASU. He continues to explore new frontiers in AI and robotics, with a focus on developing innovative and efficient solutions. Mariano currently resides in Tennessee.

Tentative talk title: Morphology-Agnostic Policy Learning: Past, Present, and Future Directions

Dorsa Sadigh

Bio: Dr. Dorsa Sadigh is an Assistant Professor in the Computer Science Department at Stanford University. Her research interests lie at the intersection of robotics, machine learning, and control theory. Specifically, her group is interested in developing efficient algorithms for safe, reliable, and adaptive human-robot and generally multi-agent interactions. She received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley in 2017, and her bachelor’s degree in EECS at UC Berkeley in 2012.

Tentative talk title: Learning Generalizable Policies from Extreme Cross-Embodiment Data

Oier Mees

Bio: Dr. Oier Mees is a PostDoc at UC Berkeley working with Prof. Sergey Levine. He received his PhD in Computer Science (summa cum laude) in 2023 from the Freiburg University supervised by Prof. Dr. Wolfram Burgard. His research focuses on robot learning, to enable robots to intelligently interact with both the physical world and humans, and improve themselves over time. These days, he is particularly interested in how we can build self-improving embodied foundation models that can generalize the same way humans do. His research has been nominated for (and received) several Best Paper Awards, including ICRA and RA-L. Previously, he also spent time at NVIDIA AI interning with Dieter Fox.

Tentative talk title: Embodied Multimodal Intelligence with Foundation Models

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