3 accepted papers
Active scene reconstruction aims to autonomously recover the fine-grained appearance and structural details of a complex unknown scenes. Existing approaches based on 2D topological or voxel-based abstractions often scale poorly to large environments and rely heavily on handcrafted features and heuri…
Reinforcement learning (RL) has renewed interest in end-to-end robot navigation, yet dynamic, crowd-like scenes remain difficult due to partial observability and the fragility of online tracking. We introduce GPTS-Nav, a Graph-Privileged Teacher-Student RL framework for LiDAR-only navigation. A grap
Autonomous exploration in large-scale, unknown environments remains a significant challenge in mobile robotics. In this paper, we propose a scalable exploration framework that integrates heterogeneous topological representations, lightweight global-local graph reasoning, and reinforcement learning.