3 accepted papers
In this letter, we present a deep reinforcement learning-based dimension-configurable local planner (DRL-DCLP) for solving robot navigation problems. DRL-DCLP is the first neural-network local planner capable of handling rectangular differential-drive robots with varying dimension configurations wit
This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation approach called scenario augmentation, which enables robots to navigate effectively across diverse settings without alterin
Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Conventional DRL approaches predominantly learn forward-motion policies, causing robots to become trapped in complex enviro