Learning-Based Sparsification of Dynamic Graphs in Robotic Exploration Algorithms

ArXi:2604.16509v1 Announce Type: cross Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning. However, these graphs grow rapidly, accumulating redundant information and impacting performance. We present a transformer-based framework trained with Proximal Policy Optimization (PPO) to prune these graphs during exploration, limiting their growth and reducing the accumulation of excess information.