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Jungjin Lee

Publications and source records attributed to Jungjin Lee.

12 recordsLinked to original sources
arXiv2026

WRAP: Wasserstein-Robust Adaptive Plug-in for Robot Localization

Robotic localization under changing sensing conditions can suffer from biased errors and miscalibrated covariances. We present WRAP, an adapter-agnostic Wasserstein-robust plug-in for nonlinear extended Kalman filter (EKF) and error-state Kalman filter (ESKF) stacks. A causal module supplies time-varying effective process and measurement statistics; a mean-preserving Wasserstein local update then computes least-favorable covariances and a robust gain without changing the propagation model, residual, or retraction. This separates mean adaptation from covariance robustification and uses distinct radii for propagation and sensing. On 18 UWB--IMU sequences held out from adapter training, adapter-only and WRAP reduce mean 3-D position RMSE by $19.8\%$ and $27.4\%$ relative to the nominal ESKF; an isotropic ablation reaches $19.5\%$, linking the incremental gain to directional process-covariance redistribution. An in-sample GNSS--INS study shows that mean adaptation provides most of the accuracy gain, while DR improves consistency and mitigates over-tightened classical covariance estimates. The robust solve takes 0.05 ms for UWB and 2.92 ms for GNSS on a Jetson Orin Nano.

cs.RO↗
arXiv2026

Residual-Aware Distributionally Robust EKF: Absorbing Linearization Mismatch via Wasserstein Ambiguity

The extended Kalman filter (EKF) is a cornerstone of nonlinear state estimation, yet its performance is fundamentally limited by noise-model mismatch and linearization errors. We develop a residual-aware distributionally robust EKF that addresses both challenges within a unified Wasserstein distributionally robust state estimation framework. The key idea is to treat linearization residuals as uncertainty and absorb them into an effective uncertainty model captured by a stage-wise ambiguity set, enabling noise-model mismatch and approximation errors to be handled within a single formulation. This approach yields a computable effective radius along with deterministic upper bounds on the prior and posterior mean-squared errors of the true nonlinear estimation error. The resulting filter admits a tractable semidefinite programming reformulation while preserving the recursive structure of the classical EKF. Simulations on coordinated-turn target tracking and uncertainty-aware robot navigation demonstrate improved estimation accuracy and safety compared to standard EKF baselines under model mismatch and nonlinear effects.

eess.SY↗
arXiv2025

KoopCast: Trajectory Forecasting via Koopman Operators

We present KoopCast, a lightweight yet efficient model for trajectory forecasting in general dynamic environments. Our approach leverages Koopman operator theory, which enables a linear representation of nonlinear dynamics by lifting trajectories into a higher-dimensional space. The framework follows a two-stage design: first, a probabilistic neural goal estimator predicts plausible long-term targets, specifying where to go; second, a Koopman operator-based refinement module incorporates intention and history into a nonlinear feature space, enabling linear prediction that dictates how to go. This dual structure not only ensures strong predictive accuracy but also inherits the favorable properties of linear operators while faithfully capturing nonlinear dynamics. As a result, our model offers three key advantages: (i) competitive accuracy, (ii) interpretability grounded in Koopman spectral theory, and (iii) low-latency deployment. We validate these benefits on ETH/UCY, the Waymo Open Motion Dataset, and nuScenes, which feature rich multi-agent interactions and map-constrained nonlinear motion. Across benchmarks, KoopCast consistently delivers high predictive accuracy together with mode-level interpretability and practical efficiency.

cs.LG↗
arXiv2025

Egocentric Conformal Prediction for Safe and Efficient Navigation in Dynamic Cluttered Environments

Conformal prediction (CP) has emerged as a powerful tool in robotics and control, thanks to its ability to calibrate complex, data-driven models with formal guarantees. However, in robot navigation tasks, existing CP-based methods often decouple prediction from control, evaluating models without considering whether prediction errors actually compromise safety. Consequently, ego-vehicles may become overly conservative or even immobilized when all potential trajectories appear infeasible. To address this issue, we propose a novel CP-based navigation framework that responds exclusively to safety-critical prediction errors. Our approach introduces egocentric score functions that quantify how much closer obstacles are to a candidate vehicle position than anticipated. These score functions are then integrated into a model predictive control scheme, wherein each candidate state is individually evaluated for safety. Combined with an adaptive CP mechanism, our framework dynamically adjusts to changes in obstacle motion without resorting to unnecessary conservatism. Theoretical analyses indicate that our method outperforms existing CP-based approaches in terms of cost-efficiency while maintaining the desired safety levels, as further validated through experiments on real-world datasets featuring densely populated pedestrian environments.

cs.RO↗
arXiv2025

Concert Interaction Translation: Augmenting VR Live Concert Experience using Chat-Driven Artificial Collective Reactions

Computer-mediated concerts can be enjoyed on various devices, from desktop and mobile to VR devices, often supporting multiple devices simultaneously. However, due to the limited accessibility of VR devices, relatively small audience members tend to congregate in VR venues, resulting in diminished unique social experiences. To address this gap and enrich VR concert experiences, we present a novel approach that leverages non-VR user interaction data, specifically chat from audiences watching the same content on a live-streaming platform. Based on an analysis of audience reactions in offline concerts, we designed and prototyped a concert interaction translation system that extracts the level of engagement and emotions from chats and translates them to collective movements, cheers, and singalongs of virtual audience avatars in a VR venue. Our user study (n=48) demonstrates that our system, which combines both movement and audio reactions, significantly enhances the sense of immersion and co-presence than the previous method.

cs.HC↗
arXiv2025

VTuber's Atelier: The Design Space, Challenges, and Opportunities for VTubing

VTubing, the practice of live streaming using virtual avatars, has gained worldwide popularity among streamers seeking to maintain anonymity. While previous research has primarily focused on the social and cultural aspects of VTubing, there is a noticeable lack of studies examining the practical challenges VTubers face in creating and operating their avatars. To address this gap, we surveyed VTubers' equipment and expanded the live-streaming design space by introducing six new dimensions related to avatar creation and control. Additionally, we conducted interviews with 16 professional VTubers to comprehensively explore their practices, strategies, and challenges throughout the VTubing process. Our findings reveal that VTubers face significant burdens compared to real-person streamers due to fragmented tools and the multi-tasking nature of VTubing, leading to unique workarounds. Finally, we summarize these challenges and propose design opportunities to improve the effectiveness and efficiency of VTubing.

cs.HC↗
arXiv2021

An endpoint estimate of the bilinear paraboloid restriction operator

In Fourier restriction problems, a cone and a paraboloid are model surfaces. The sharp bilinear cone restriction estimate was first shown by Wolff, and later the endpoint was obtained by Tao. For a paraboloid, the sharp $L^2$ bilinear restriction estimate was obtained by Tao, but the endpoint was remained open. In this paper we prove the endpoint $L^2$ bilinear restriction estimate for a paraboloid.

math.CA↗
arXiv2021

A global space-time estimate for dispersive operators through its local estimate

We will show that a local space-time estimate implies a global space-time estimate for dispersive operators. In order for this implication we consider a Littlewood-Paley type square function estimate for dispersive operators in a time variable and a generalization of Tao's epsilon removal lemma in mixed norms. By applying this implication to the fractional Schrodinger equation in R^{2+1} we obtain the sharp global space-time estimates with optimal regularity from the previous known local ones.

math.AP↗
arXiv2019

Global Kato type smoothing estimates via local ones for dispersive equations

In this paper we show that the local Kato type smoothing estimates are essentially equivalent to the global Kato type smoothing estimates for some class of dispersive equations including the Schrödinger equation. From this we immediately have two results as follows. One is that the known local Kato smoothing estimates are sharp. The sharp regularity ranges of the global Kato smoothing estimates are already known, but those of the local Kato smoothing estimates are not. Recently, Sun, Trélat, Zhang and Zhong have shown it only in spacetime $\mathbb R \times \mathbb R$. Our result resolves this issue in higher dimensions. The other one is the sharp global-in-time maximal Schrödinger estimates. Recently, the pointwise convergence conjecture of the Schrödinger equation has been settled by Du--Guth--Li--Zhang and Du--Zhang. For this they proved related sharp local maximal Schrödinger estimates. By our result, these lead to the sharp global-in-time maximal Schrödinger estimates.

math.CA↗
arXiv2018

A trilinear approach to square function and local smoothing estimates for the wave operator

The purpose of this paper is to improve the known estimates for Mockenhaupt's square function in $\mathbb R^3$ and for Sogge's local smoothing in $\mathbb R^{2+1}$ spacetime. For this we use the trilinear approach of S. Lee and A. Vargas for the cone multiplier with some trilinear estimates obtained from the $\ell^2$ decoupling theorem and multilinear restriction theorem.

math.CA↗
arXiv2017

Bilinear restriction estimates for surfaces of codimension bigger than one

In connection with the restriction problem in $\mathbb R^n$ for hypersurfaces including the sphere and paraboloid, the bilinear (adjoint) restriction estimates have been extensively studied. However, not much is known about such estimates for surfaces with codimension (and dimension) larger than one. In this paper we show sharp bilinear $L^2 \times L^2 \to L^q$ restriction estimates for general surfaces of higher codimension. In some special cases, we can apply these results to obtain the corresponding linear estimates.

math.CA↗