4 accepted papers
Navigation of mobile robots in dynamic environments with pedestrian traffic poses a significant challenge in the development of autonomous mobile service robots. Recently, deep reinforcement learning-based methods have been actively studied and have outperformed traditional rule-based approaches, ow…
Mobile robot navigation in crowded environments with pedestrians is a crucial challenge in realizing service robots that can assist people in their daily lives. Navigation methods for mobile robots in environments employing deep reinforcement learning have been extensively studied. However, addressi…
In this paper, we introduce a framework for imitation learning in navigation that enables policy learning from one-shot images without a physical robot and facilitates the transfer of this policy from simulation to reality. Utilizing Neural Radiance Fields (NeRF), our approach generates a simulated…
Detection of spatial change around a robot is indispensable in several robotic applications, such as search and rescue, security, and surveillance. The present paper proposes a fast spatial change detection technique for a mobile robot using an on-board RGB-D/stereo camera and a highly precise 3D ma…