Internetional Robot Learning Symposium Seoul

Artificial Intelligence Institute Seoul National University (AIIS)
1 Gwanak-ro, Gwanak-gu, Seoul 08826
Building 303, 2nd Floor

September 26, 2025

Current robotic systems have fallen short of public expectations, with most deployed solutions still operating within confined behavioral boundaries. The transition toward robots functioning seamlessly in everyday environments presents three key challenges that our symposium will explore: enabling flexible task execution through multimodal interactions, developing computational frameworks that mirror human cognitive flexibility, and creating systems that continuously refine their skills while maintaining operational safety.

In this third edition of the international robot learning symposium, we expand our focus: from tactile sensing and contact-rich manipulation, to scalable humanoid vision-language-action models, to precise and constraint-aware robot behaviors. Our speakers will showcase pioneering approaches that blend self-supervised learning, large language models, adaptive control strategies, high-resolution tactile sensing, and constraint-based planning to address these fundamental challenges in modern robotics.

Tentative schedule

(The detailed schedule will be updated soon)

Speakers

Nima Fazeli

Nima Fazeli

Nima Fazeli is an Assistant Professor of Robotics, Computer Science (EECS), and Mechanical Engineering at the University of Michigan, and an Amazon Scholar with Amazon Robotics. He leads the Manipulation and Machine Intelligence (MMint) Lab, which focuses on intelligent and dexterous robotic manipulation through advances in sensing, learning, and control. Nima received his Ph.D. from MIT in 2019, where he worked with Prof. Alberto Rodriguez. He earned his M.Sc. from the University of Maryland, College Park in 2014, where his research focused on modeling the human (and occasionally swine) arterial tree for applications in cardiovascular disease, diabetes, and cancer diagnosis. His work has been recognized with the NSF CAREER Award, support from the National Robotics Initiative and NSF Advanced Manufacturing, and the Rohsenow Fellowship. His research has also been featured in major media outlets including The New York Times, CBS, CNN, and BBC.

Majid Khadiv

Majid Khadiv

Majid Khadiv is an assistant professor in the school of Computation, Information and Technology (CIT) at TUM. He leads the chair of AI Planning in Dynamic Environments and is also a member of the Munich Institute of Robotics and Machine Intelligence (MIRMI). Prior to joining TUM, he was a research scientist at the Empirical Inference Department at the Max Planck Institute for Intelligent systems. Before that he was a postdoctoral researcher in the Machines in Motion, a joint laboratory between New York University and Max Planck Institute. Since the start of his PhD in 2012, he has been performing research on motion planning, control and learning for legged robots ranging from quadrupeds, lower-limb exoskeleton up to humanoid robots.

Daehyung Park

Daehyung Park

Daehyung Park is an associate professor at the School of Computing, KAIST, Korea, leading the Robust Intelligence and Robotics Laboratory (RIRO Lab). His research lies at the intersection of mobile manipulation, artificial intelligence, and human-robot interaction to advance collaborative robot technologies. Prior to joining KAIST, he had been a postdoctoral associate in Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT), working on inverse constraint learning and semantic knowledge estimation. He received a Ph.D. in Robotics at Georgia Institute of Technology, an M.S. from the University of Southern California, and a B.S. from Osaka University. Prior to joining his Ph.D., he served as a Robotics Researcher at Samsung Electronics Inc from 2008-2012. His work was awarded the Outstanding Planning Paper Award at ICRA 2023, Google Research Scholar Award 2022, an Outstanding Navigation Award Finalist at ICRA 2022, etc. See more at: https://rirolab.kaist.ac.kr/