Alexiy Buynitsky — accepted AI-conference papers

2025

Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments

Wei Han Chen, Yuchen Liu, Alexiy Buynitsky, Ahmed H. Qureshi

IROS 2025

Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with resolution control and scalability, while imitation learning-based methods require a large amount of demonstration data. Ac

Cited by 0 Source ↗Scholar ↗

2025

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments

Yuchen Liu, Alexiy Buynitsky, Ruiqi Ni, Ahmed H. Qureshi

IROS 2025

Physics-informed Neural Motion Planners (PiN- MPs) provide a data-efficient framework for solving the Eikonal Partial Differential Equation (PDE) and representing the cost-to-go function for motion planning. However, their scalability remains limited by spectral bias and the complex loss landscape o

Cited by 2 Source ↗Scholar ↗