6 papers
Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead
Junghyun Kim, Ngseo Kim, ChungWoo Lee +7
Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific factors rather than task-relevan…
Connectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning
Suhyun Jeon, Yumin Lim, Woo-Jeong Baek +3
The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasib…
TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards
Hokyun Lee, Woo-Jeong Baek, Junhyeok Cha +1
With the growing employment of learning algorithms in robotic applications, research on reinforcement learning for bipedal locomotion has become a central topic for humanoid roboti…
SUPER -- A Framework for Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
Johannes A. Gaus, Daniel Häufle, Woo-Jeong Baek
While many visual odometry (VO), visual-inertial odometry (VIO), and SLAM systems achieve high accuracy, the majority of existing methods miss to assess risks at runtime. This pape…
GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
Johannes A. Gaus, Junheon Yoon, Woo-Jeong Baek +3
This paper presents the framework \textbf{GUARD} (\textbf{G}uided robot control via \textbf{U}ncertainty attribution and prob\textbf{A}bilistic kernel optimization for \textbf{R}is…
Reactive Model Predictive Contouring Control for Robot Manipulators
Junheon Yoon, Woo-Jeong Baek, Jaeheung Park
This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-coll…