4 accepted papers
Despite the prevalence of deep reinforcement learning (DRL) for navigation in dynamic environments, existing end-to-end DRL methods still struggle to effectively balance global planning with local obstacle avoidance and to perceive the dynamics of obstacles. Meanwhile, external detection methods are
Navigating in environments with both static obstacles and dense crowds of pedestrians using low-cost sensors and lightweight controllers is crucial for the widespread application of mobile robots, where the key challenge is the localization and tracking of dynamic obstacles using only sensor reading
Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic exploration, many methods either assume access to low-dimensional states or lack task-aware exploration strategies, ther…
Low-light image enhancement (LLIE) aims to recover illumination and improve the visibility of low-light images. Conventional LLIE methods often produce poor results because they neglect the effect of noise interference. Deep learning-based LLIE methods focus on learning a mapping function between lo…