Junwon Seo

I am a PhD student in the Robotics Institute at Carnegie Mellon University (CMU), advised by Prof. Andrea Bajcsy.

I work at the intersection of robotics, machine learning, and information theory to build robots that can operate robustly under uncertainty. I am particularly interested in quantifying and mitigating uncertainty in learning-based robotic systems (e.g., world models, robotic policies), leveraging information-theoretic methods (e.g., uncertainty quantification) and decision-theoretic methods (e.g., robust control), with applications to embodied AI systems such as robotic manipulation and autonomous navigation.

Before joining CMU, I was a research officer at the Defense AI Center of the Agency for Defense Development (ADD), where I researched uncertainty-aware navigation in complex and unknown environments. I completed my bachelor's degree (Summa Cum Laude) in Computer Science and Engineering at the Seoul National University (SNU).

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  [Sep 2026]

  One paper accepted to CoRL 2026! StressDream steers video world models toward plausible, high-impact outcomes by optimizing noise of the diffusion-based video world models at inference time, enabling robust evaluation and improvement of robotic policies. See the project page.
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  [July 2026]

  I gave my research qualifier talk on "Robust Visuomotor Policy Learning in Uncertain World Models", and became a master officially. See video here.
• [Mar 2026] I gave a guest lecture on "Implementing Uncertainty Quantification for Robotics" in the Embodied Artificial Intelligence Safety (16-886) at CMU. Lecture materials: [code] [tutorial notebook].
• [Feb 2026] One paper accepted to ICRA 2026! AnySafe is a constraint-parameterized latent safety filter that can adapt to user-specified safety constraints at runtime.
• [Jan 2026] One paper accepted to RA-L! E2-BKI is an uncertainty-aware semantic mapping using Gaussian representations and Evidential Deep Learning.
• [Nov 2025] One paper accepted to WACV 2026! OW-Rep is an open world object detection framework that jointly detects unknown objects and learns instance embeddings, enabling the detector to capture fine-grained semantic relationships between instances that are useful for downstream tasks.
• [Jul 2025] One paper accepted to CoRL 2025! UNISafe is an uncertainty-aware latent safety filter that unifies reachability analysis in a latent world model with OOD detection to proactively steer robots away from both known and out-of-distribution failures.
• [Sep 2024] I started my PhD in the Robotics Institute at Carnegie Mellon University (CMU), happily advised by Prof. Andrea Bajcsy.
• [Jul 2024] Two papers accepted to IROS 2024! One is using evidential deep learning for uncertainty-aware semantic mapping (selected as Best Cognitive Paper Finalist), and the other is using meta-learning for online adaptation of traversability cost maps for off-road navigation.
• [May 2024] I finished my military service in Agency for Defense Development (ADD) as a Research Officer for National Defense (ROND), where I researched uncertainty-aware navigation in unstructured and unknown environments. Junwon is free!
• [May 2024] Presented three papers at ICRA 2024! Our RA-L paper introduces self-supervised traversability estimation for off-road environments; another work explores domain adaptive object detection in real-world adverse weather; and a workshop paper proposes uncertainty-aware semantic mapping using Dempster-Shafer theory of evidence.
• [Oct 2023] Presented my RA-L paper on learning vehicle-specific traversability with self-supervised learning at IROS 2023.
• [Mar 2023] Our paper on bridging active exploration and uncertainty-aware deployment accepted to RSS 2023!

* indicates equal contribution

Steering video world models toward plausible, high-impact outcomes, enabling robust evaluation and improvement of robotic policies.

Constraint-parameterized latent safety filters that can adapt to user-specified safety constraints at runtime.

Evidential Ellipsoidal Bayesian Kernel Inference for uncertainty-aware continuous 3D semantic mapping.

Uncertainty-aware latent safety filters that unify reachability analysis in a latent world model with OOD detection to prevent both known and unseen safety hazards.

Open World Object Detection framework that jointly detects unknown objects and learns instance embeddings.

Uncertainty-aware semantic BKI mapping framework for robust deployments in off-road environments using Evidential Deep Learning.

Meta-learning framework for learning a global model that accurately predicts off-road terrain traversability.

UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation.

Unsupervised domain adaptation for robust object detection in real-world adverse weather conditions.

Active exploration for learning vehicle dynamics model and uncertainty-aware deployment.

Visual terrain traversability learning in off-road that utilizes only self-supervision without human annotations.

LiDAR-based self-supervised traversability estimation framework that can be easily scalable to various vehicles and sensor types.

Handling uncertainty problems in self-supervised traversability estimation with metric learning.

Perception and control algorithms for off-road navigation: learning-based traversability cost map, semantic terrain classification map, moving object detection and tracking, and vehicle dynamics learning.

Built an autonomous driving dataset with multi-spectral images and LiDAR in adverse weather conditions and developed a robust traversable area detection and object detection algorithm.

Developed a learning-based traversability map for autonomous robotic exploration under subterranean environments.

The light stage system and face normal map reconstruction algorithm for 3D face reconstruction with high fidelity.

A Structured-light 3D scanner for high-quality hair and face geometry reconstruction.

Website template adapted from Jon Barron's website