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Recent progress in Physical AI has been driven largely by models: vision-language-action architectures, reinforcement learning, and large-scale demonstration data collected through teleoperation. Robots, however, inherently involve mechanical contact, and current vision-centric approaches face fundamental limitations in physical interaction.
This talk addresses contact perception as the missing element. It first examines why teleoperation-based, vision-only demonstrations fail to capture physical interaction, and then reviews tactile and force sensing that fills this gap, from fingertip sensors to whole-body robot skin. It further argues that mechanical compliance is what makes such interaction safe, and that haptic feedback should close the demonstration loop if robots are expected to learn from contact. The talk concludes with how foundation models are beginning to incorporate touch.
Bio:
Kyungseo Park is an Assistant Professor in the Department of Robotics and Mechatronics Engineering at DGIST, where he leads the Interactive Robot Lab. His research addresses tactile sensing, safe and dependable robots, whole-body multimodal perception, and contact-rich control for physical human-robot interaction. He received his B.S., M.S., and Ph.D. from KAIST, and was a postdoctoral researcher at the KIMLAB, University of Illinois Urbana-Champaign.
Image stylization aims to transform the visual appearance of an image while preserving its content and semantic information.
This tutorial introduces the evolution of image stylization from early neural style transfer to recent diffusion-based and personalized generation methods.
We will cover key concepts and methods, including content-style representation, reference-guided stylization, and personalization using diffusion models.
Finally, the tutorial will discuss practical applications and open challenges, including style evaluation, content leakage, and dataset construction.
Jae Woong Soh is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at Gwangju Institute of Science and Technology (GIST).
He received the B.S. and Ph.D. degrees in Electrical and Computer Engineering from Seoul National University in 2016 and 2021, respectively.
Before joining GIST, he worked as a Staff Engineer at Samsung Research. His research interests include image processing, computer vision, and generative models.
Spatial reasoning is a critical capability for embodied agents, enabling them to understand, navigate, and interact with complex environments. In this talk, I will discuss key challenges, including frame-of-reference reasoning and long-term spatial reasoning, as well as the abilities agents need to operate robustly in partially observable environments.
Hyounghun Kim is an assistant professor in the Graduate School of Artificial Intelligence and the Department of Computer Science and Engineering at POSTECH. He earned his Ph.D. from the Department of Computer Science at UNC-Chapel Hill, advised by Prof. Mohit Bansal. His research spans natural language processing and multimodal learning with focus on large language models, conversational agents, commonsense reasoning, image/video QA, as well as embodied AI. He has served as a senior action editor, action editor/area chair, and reviewer for ACL Rolling Review and AI top conferences. His professional experience includes internships at Adobe Research and Amazon Alexa AI, and a position as a software engineer at Samsung Electronics.
