14 papers
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…
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…
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…
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…
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…