News

The professor, Youngju, and Taegeun gave a talk titled “Physics-Based Visual Intelligence and Robotic Action Technologies for Physical AI (Physical AI를 위한 물리 기반 시각지능과 로봇 행동 기술),” discussing recent advances that enable AI systems to understand and interact with the physical world through perception and action.

Kyubeom‘s paper (ICLR 2026 oral) was selected for an oral presentation at KCCV and our professor gave a presentation about the work.

Our lab is gaining attention for advances in Physical AI technologies. By connecting visual perception, physical-world understanding, prediction, and action planning, our lab is laying key groundwork for robots that can operate in real environments. These achievements, presented through four papers (two highlight papers and two oral papers) at ICLR 2026 and CVPR 2026, highlight the lab’s growing leadership in Physical AI.

KAIST School of Computing announced that Youngju‘s paper, titled “GLINT,” has been selected as one of just 74 Award Candidates out of over 4,000 submissions at CVPR 2026. This breakthrough technology solves a long-standing computer vision challenge by separating reflected and transmitted light to accurately reconstruct 3D environments containing transparent objects. See the article for details.

Our Lab attended the Korea Computer Congress (KCC) 2026 to participate in the Research Performance Exchange for the 2026 IITP “Human-Oriented Next-Generation Challenging AI Technology Development” project.

Out of 20 initial participating teams, our lab was one of three selected teams that proceeds to the 2nd phase of the project, after successfully passing the program’s phase evaluation. During the session, the final three teams shared their core research milestones and discussed strategic directions for the next phase of the project.

Our lab has successfully completed a research project with Hanwha Aerospace, titled “Development of Core Technologies for Autonomous Driving in Unstructured Off-Road Environments.”

During this project, our team was responsible for path planning that incorporates unstructured terrain information. Specifically, we developed terrain-aware path planning, exploratory driving, and dynamic obstacle avoidance systems.

The developed technologies were successfully demonstrated in a real-world environment at the Hanwha Aerospace Boeun Plant.

Congratulations to Prof. Jae-Pil Heo and his team!

Their paper SeaCache has been recognized as a CVPR 2026 Best Paper Award Finalist: SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models.

I am particularly delighted to see this recognition earned by Prof. Heo, one of my former Ph.D. students. As an advisor, there are few moments more rewarding than seeing former students grow into leading researchers and make impactful contributions to our field. Congratulations again to Jae-Pil and all the authors on this well-deserved honor.

Andrew presented his paper, “CLaD: Planning with Grounded Foresight via Cross-Modal Latent Dynamics” at CVPR 2026, held from June 3rd – 7th, 2026, in Denver, USA.

He suggested CLaD, a framework that models the joint evolution of proprioceptive and semantic states under robotic actions through asymmetric cross-attention. By enforcing consistency over cross-modal transitions and using self-supervised objectives, the model predicts grounded latent foresights without experiencing representation collapse. These predicted foresights condition a diffusion policy for action generation, achieving a high success rate on long-horizon tasks with significantly fewer parameters than large vision-language-action (VLA) models.

Sebin and Jumin presented their paper, “Visual-RRT: Finding Paths toward Visual-Goals via Differentiable Rendering” at CVPR 2026, held from June 3rd – 7th, 2026, in Denver, USA.

Visual-RRT (vRRT) is a novel robot motion planning technology that enables robots to plan paths based solely on a target image or visual observations, eliminating the need for explicit numeric target joint values required by traditional RRT planners. By combining sampling-based RRT exploration with differentiable robot rendering, vRRT searches for paths that minimize the visual discrepancy between the target image and the current rendered robot state. The method enhances exploration efficiency through a frontier-based strategy that prioritizes visually promising nodes and an inertial gradient tree expansion technique that leverages prior optimization data.

Youngju presented his paper, “GLINT: Modeling Scene-Scale Transparency via Gaussian Radiance Transport” at CVPR 2026, held from June 3rd – 7th, 2026, in Denver, USA. The paper was presented as an oral at CVPR 2026 and selected as a Best Paper Award Candidate, corresponding to roughly the top 3.4% and 1.8% of accepted papers, respectively.

GLINT addresses the challenge of reconstructing real-world scenes containing transparent and reflective surfaces, where conventional 3D Gaussian Splatting often fails to preserve the physical presence of glass. By disentangling the visible interface from transmitted and reflected radiance, GLINT enables more faithful and physically consistent reconstruction of complex scenes with glass, windows, and specular objects.

Chanmi presented her paper, “Beyond the Patch: Exploring Vulnerabilities of Visuomotor Policies via Viewpoint-Consistent 3D Adversarial Object” at ICRA 2026 Conference, held from June 1st – 5th, 2026, in Vienna, Austria.

She presented a study on 3D adversarial attacks to identify the vulnerability of policies in continuously changing camera settings, such as visuomotor policies with wrist-cam configurations.

Jiwoo presented his paper, “Uncertainty-Aware Non-Prehensile Manipulation with Mobile Manipulator under Object-Induced Occlusion” at ICRA 2026 Conference, held from June 1st – 5th, 2026, in Vienna, Austria.

He suggested CURA-PPO, a reinforcement learning framework for safe non-prehensile manipulation under occlusions, jointly estimating collision risk and uncertainty to operate reliably in partially observable environments.

Minsung presented his paper, “Phase-Aware Policy Learning for Skateboard Riding of Quadruped Robots” at ICRA 2026 Conference, held from June 1st – 5th, 2026, in Vienna, Austria.

He suggested PAPL (Phase-Aware Policy Learning), a reinforcement learning framework that enables quadruped robots to ride skateboards by leveraging phase-aware policy modulation while maintaining a single unified control policy.

Our research group has been selected for two major InnoCORE research initiatives, marking an important step toward advancing next-generation AI and robotics research.

1. AI Meta-Scientist Initiative

Vision: Toward AI systems that autonomously generate new knowledge

The AI Meta-Scientist Initiative aims to develop a new class of AI systems capable of scientific discovery and knowledge creation.

The project focuses on integrating:
– Advanced foundation models
– Reasoning capabilities
– System-level orchestration
to realize an AI system operating at the level of a research institute director (meta-scientist-level intelligence).

2. 5D AI Robotics Initiative (5D AI-RI)

Vision: Next-generation multimodal robot AI for real-world adaptation

The 5D AI Robotics Initiative (5D AI-RI) aims to develop:
– World-leading 5D multimodal robot foundation models
– Real-world adaptive robotic platforms
with the goal of advancing embodied AI and fostering national-level
top-tier young researchers in robotics and AI.
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Program Highlights

Duration: 5 years
Support: Up to 2 postdoctoral researchers per year
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These initiatives will enable our group to push the frontier of physical intelligence, embodied AI, and AI-driven scientific discovery, while contributing to the development of next-generation AI talent and technologies.