Pose-guided End-to-end Visual Navigation

Abstract

End-to-end visual navigation based on deep reinforcement learning (DRL) has recently attracted much attention. For most existing navigation methods, a robot moves along only fixed directions (e.g., up, down, left and right) on a grid. Obviously, they are not flexible and efficient, which worsens the navigation performance (i.e., the distance of movement and times of rotation). To address this problem, we propose a novel pose-guided end-to-end visual navigation framework, which is flexible and efficient. In the pose-guided navigation framework, a robot can move along arbitrary directions, which are determined by poses between adjacent objects. Further, to select a proper motion and finally form an optimal path, we propose a DRL based action-selected strategy, where a dynamic action select space on the basis of deep siamese actor-critic network is developed. Besides, to validate the proposed method, we propose a novel pose-guided dataset. Experimental results demonstrate that the proposed method outperforms the state of the arts in both flexibility and efficiency.