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Junyoung Lee

Junyoung Lee appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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3 published item(s)

preprint2026arXiv

OmniRobotHome: A Multi-Camera Platform for Real-Time Multiadic Human-Robot Interaction

Human-robot collaboration has been studied primarily in dyadic or sequential settings. However, real homes require multiadic collaboration, where multiple humans and robots share a workspace, acting concurrently on interleaved subtasks with tight spatial and temporal coupling. This regime remains underexplored because close-proximity interaction between humans, robots, and objects creates persistent occlusion and rapid state changes, making reliable real-time 3D tracking the central bottleneck. No existing platform provides the real-time, occlusion-robust, room-scale perception needed to make this regime experimentally tractable. We present OmniRobotHome, the first room-scale residential platform that unifies wide-area real-time 3D human and object perception with coordinated multi-robot actuation in a shared world frame. The system instruments a natural home environment with 48 hardware-synchronized RGB cameras for markerless, occlusion-robust tracking of multiple humans and objects, temporally aligned with two Franka arms that act on live scene state. Continuous capture within this consistent frame further supports long-horizon human behavior modeling from accumulated trajectories. The platform makes the multiadic collaboration regime experimentally tractable. We focus on two central problems: safety in shared human-robot environments and human-anticipatory robotic assistance, and show that real-time perception and accumulated behavior memory each yield measurable gains in both.

preprint2022arXiv

Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models

Latte (for LATent Tensor Evaluation) is a Python library for evaluation of latent-based generative models in the fields of disentanglement learning and controllable generation. Latte is compatible with both PyTorch and TensorFlow/Keras, and provides both functional and modular APIs that can be easily extended to support other deep learning frameworks. Using NumPy-based and framework-agnostic implementation, Latte ensures reproducible, consistent, and deterministic metric calculations regardless of the deep learning framework of choice.

preprint2015arXiv

Spectrum estimates of Hill's lunar problem

We investigate the action spectrum of Hill's lunar problem by observing inclusions between the Liouville domains enclosed by the regularized energy hypersurfaces of the rotating Kepler problem and Hill's lunar problem. In this paper, we reinterpret the spectral invariant corresponding to every nonzero homology class $α\in H_*(ΛN)$ in the loop homology as a symplectic capacity $c_N(M, α)$ for a fiberwise star-shaped domain $M$ in a cotangent bundle with canonical symplectic structure $(T^*N, ω_{can}=d λ_{can})$. Also, we determine the action spectrum of the regularized rotating Kepler problem. As a result, we obtain estimates of the action spectrum of Hill's lunar problem. This will show that there exists a periodic orbit of Hill's lunar problem whose action is less than $π$ for any energy $-c < -{3^{4 \over 3} \over 2}$.