5 citations · 7 across the 17 of their papers we have counts for
18 papers
DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
Sanghyuk Park, Kwanwoo Lee, Taekyung Kim +2
Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding infrastructure. Control barrie…
OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles
Taekyung Kim, Hun Kuk Park, Renya Wada +3
Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present OcclusionCBF, a safety filter tha…
Safety-aware Model Predictive Path Integral Control with Signal Temporal Logic
Yiqi Zhao, Taekyung Kim, Hideki Okamoto +4
Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-awar…
Distributed Model-Based Diffusion For Scalable Multi-Robot Trajectory Optimization
Haejoon Lee, Xinyi Wang, Taekyung Kim +1
Trajectory optimization for multi-robot systems remains a critical challenge, particularly when navigating highly non-convex, non-linear, and non-differentiable environments. While…
SEAMLiS: Visibility-Aware Safety for Perception-Limited Multi-Robot Exploration
Taekyung Kim, Rahul H Kumar, Aswin D. Menon +2
Autonomous exploration in unknown environments is typically driven by informative frontiers, viewpoints, or trajectories, while local safety controllers avoid obstacles represented…
DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
Xinyi Wang, Taekyung Kim, Bardh Hoxha +2
Planning through crowded environments under uncertain obstacle motions remains difficult, as stochastic interactions often induce overly conservative behavior or reduced efficiency…
Policy Library CBF: Finite-Horizon Safety at Runtime via Parallel Rollouts
Taekyung Kim, Hideki Okamoto, Bardh Hoxha +2
Safety-critical autonomy in unstructured environments poses significant challenges for online safety certification under evolving constraints. We propose Policy Library Control Bar…
Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach
Daniel M. Cherenson, Haejoon Lee, Taekyung Kim +1
We study how to ensure probabilistic safety for nonlinear systems under distributional ambiguity. Our approach builds on a backup-based safety filtering framework that switches bet…
Backup-Based Safety Filters: A Comparative Review of Backup CBF, Model Predictive Shielding, and gatekeeper
Taekyung Kim, Aswin D. Menon, Akshunn Trivedi +1
This paper revisits three backup-based safety filters -- Backup Control Barrier Functions (Backup CBF), Model Predictive Shielding (MPS), and gatekeeper -- through a unified compar…
Safe Model Predictive Diffusion with Shielding
Taekyung Kim, Keyvan Majd, Hideki Okamoto +3
Generating safe, kinodynamically feasible, and optimal trajectories for complex robotic systems is a central challenge in robotics. This paper presents Safe Model Predictive Diffus…
Beyond Collision Cones: Dynamic Obstacle Avoidance for Nonholonomic Robots via Dynamic Parabolic Control Barrier Functions
Hun Kuk Park, Taekyung Kim, Dimitra Panagou
Control Barrier Functions (CBFs) are a powerful tool for ensuring the safety of autonomous systems, yet applying them to nonholonomic robots in cluttered, dynamic environments rema…
BR-MPPI: Barrier-Rate Guided MPPI for Enforcing Multiple Inequality Constraints with Learned Signed Distance Fields
Hardik Parwana, Taekyung Kim, Kehan Long +4
Model Predictive Path Integral (MPPI) control provides a sampling-based framework for optimal control, while Control Barrier Functions (CBFs) provide a principled means of enforcin…
Certifiably-Correct Mapping for Safe Navigation Despite Odometry Drift
Devansh R. Agrawal, Taekyung Kim, Rajiv Govindjee +4
Accurate perception, state estimation and mapping are essential for safe robotic navigation as planners and controllers rely on these components for safety-critical decisions. Howe…
Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions
Xinyi Wang, Taekyung Kim, Bardh Hoxha +2
Robot navigation in dynamic, crowded environments poses a significant challenge due to the inherent uncertainties in the obstacle model. In this work, we propose a risk-adaptive ap…
Learning to Adapt Control Barrier Functions Under Epistemic and Aleatoric Uncertainty
Taekyung Kim, Robin Inho Kee, Dimitra Panagou
Control barrier functions (CBFs) provide a tractable mechanism for enforcing safety constraints in robotic systems, but their practical performance depends strongly on the choice o…
Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation
Taekyung Kim, Robin Inho Kee, Dimitra Panagou
Control Barrier Functions (CBFs) have become powerful tools for ensuring safety in nonlinear systems. However, finding valid CBFs that guarantee persistent safety and feasibility r…
Visibility-Aware RRT* for Safety-Critical Navigation of Perception-Limited Robots in Unknown Environments
Taekyung Kim, Dimitra Panagou
Safe autonomous navigation in unknown environments remains a critical challenge for robots with limited sensing capabilities. While safety-critical control techniques, such as Cont…
METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation
Junwon Seo, Taekyung Kim, Seongyong Ahn +1
Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject t…