13 citations · 13 across the 6 of their papers we have counts for
6 papers
PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
Youngjoon Jeong, Jihwan Yu, Minsoo Jo +2
Robot actions vary in both extent and mode: how much change they produce and what kind of change they produce. A latent action space that reflects this structure may make it easier…
Uncovering Vulnerability of Vision-Language-Action Models under Joint-Level Physical Faults
Minsoo Jo, Taeju Kwon, Junha Chun +2
Deploying Vision-Language-Action (VLA) models in real robotic systems requires robustness not only to semantic and perceptual variations, but also to embodiment-side faults that ch…
Learning to Act Robustly with View-Invariant Latent Actions
Youngjoon Jeong, Junha Chun, Taesup Kim
Vision-based robotic policies often struggle with even minor viewpoint changes, underscoring the need for view-invariant visual representations. This challenge becomes more pronoun…
Sparse Imagination for Efficient Visual World Model Planning
Junha Chun, Youngjoon Jeong, Taesup Kim
World model based planning has significantly improved decision-making in complex environments by enabling agents to simulate future states and make informed choices. This computati…
Object-Centric World Model for Language-Guided Manipulation
Youngjoon Jeong, Junha Chun, Soonwoo Cha +1
A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on vide…
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
Dongsu Zhang, Junha Chun, Sang Kyun Cha +1
We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning. The raw 3D reconstruction of an ind…