6 accepted papers
Autonomous exploration is essential for the effective deployment of quadrotors in various applications. However, existing approaches face significant challenges in large-scale environments, particularly in balancing global coverage efficiency and computational overhead. These limitations often resul
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.
Visual navigation, a fundamental challenge in mobile robotics, demands versatile policies to handle diverse environments. Classical methods leverage geometric solutions to minimize specific costs, offering adaptability to new scenarios but are prone to system errors due to their multi-modular design
Recent advancements in diffusion-based imitation learning, which shows impressive performance in modeling multimodal distributions and training stability, have led to substantial progress in various robot learning tasks. In visual navigation, previous diffusion-based policies typically generate acti…
Autonomous exploration in unknown environments is a crucial challenge for various applications of unmanned aerial vehicles (UAVs). However, in large-scale scenarios, existing methods suffer from inefficient environmental information acquisition, computationally expensive exploration planning, and in
Autonomous exploration in large-scale and complex environments is a challenging task. As the size of the environment increases, the significant overhead of exploration algorithms could overwhelm the computational capability of mobile platforms, prohibiting timely response to environmental changes. M