5 papers
Agentic Open RAN: A Deterministic and Auditable Framework for Intent-Driven Radio Control
Hengxu Li, Dongkuan Xu, Mingzhe Chen +1
Large language models (LLMs) open new possibilities for agentic control in Open RAN, allowing operators to express intents in natural language while delegating low-level execution…
Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap
Ruiqi Ni, Yuchen Liu, Ahmed H. Qureshi
The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer…
Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown Environments
Wei Han Chen, Yuchen Liu, Alexiy Buynitsky +1
Robot navigation in large, complex, and unknown indoor environments is a challenging problem. The existing approaches, such as traditional sampling-based methods, struggle with res…
Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments
Yuchen Liu, Alexiy Buynitsky, Ruiqi Ni +1
Physics-informed Neural Motion Planners (PiNMPs) provide a data-efficient framework for solving the Eikonal Partial Differential Equation (PDE) and representing the cost-to-go func…
Physics-informed Neural Mapping and Motion Planning in Unknown Environments
Yuchen Liu, Ruiqi Ni, Ahmed H. Qureshi
Mapping and motion planning are two essential elements of robot intelligence that are interdependent in generating environment maps and navigating around obstacles. The existing ma…