Wonjun Jo

I am a Ph.D. candidate in the Department of Electrical Engineering at POSTECH. I conduct my research at the Advanced Machine Intelligence (AMI) Lab at KAIST, advised by Prof. Tae-Hyun Oh.

I am interested in Self-supervised Learning and Multi-modal Learning for preserving and improving the generalization capability of World Models, toward robust Embodied AI and real-world robotics.

More specifically, my recent research focuses on Vision-Language-Action (VLA) and World Action Models (WAM) for robotics, with additional interest in sound, tactile, and force signals for holistic world understanding.

Embodied AI
Self-supervised Learning
Multi-modal Learning
World Models

Email | CV | Scholar | GitHub | LinkedIn

Research

Comparison of behavior cloning fine-tuning with and without Base Action-Field Regularization for controlling policy drift

When Fine-Tuning Drifts: Mitigating Policy Drift with Base Action-Field Regularization
Wonjun Jo, Nam Hyeon-Woo, Yohan Park, Hyunwoo Ha, Tae-Hyun Oh
Under review, 2026
Project Page | Paper

Mitigating policy drift for robust few-shot fine-tuning of pretrained robot policies.

Diagram of LiDAR-anchored collaborative distillation for robust 2D representations

LiDAR-Anchored Collaborative Distillation for Robust 2D Representations
Wonjun Jo, Hyunwoo Ha, Kim Ji-Yeon, Hawook Jeong, Tae-Hyun Oh
IEEE Robotics and Automation Letters (RA-L), 2026
Project Page | Paper

Improving the robustness and generalization of self-supervised visual representations with LiDAR guidance.

DarkQA low-light visual question answering benchmark overview

DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes
Yohan Park, Hyunwoo Ha, Wonjun Jo, Tae-Hyun Oh
IEEE Robotics and Automation Letters (RA-L), 2026
Project Page | Paper

Benchmarking the robustness of embodied vision-language models under low-light conditions.

Collaborative distillation method for lighting-robust and 3D-aware representations

Self-Supervised Collaborative Distillation: Enhancing Lighting Robustness and 3D Awareness
Wonjun Jo, Hyunwoo Ha, Kim Ji-Yeon, Hawook Jeong, Tae-Hyun Oh
Workshop on Wild3D, IEEE/CVF International Conference on Computer Vision (ICCV), 2025
Paper

Improving pretrained visual representations for lighting robustness and 3D-aware generalization.

Overview of simple remedies for image-to-LiDAR representation learning

The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning
Wonjun Jo, Kwon Byung-Ki, Kim Ji-Yeon, Hawook Jeong, Kyungdon Joo, Tae-Hyun Oh
Asian Conference on Computer Vision (ACCV), 2024
Project Page | Paper

Improving self-supervised image-to-LiDAR representation learning for robust 3D perception.

Wonjun Jo

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