Abstract
Target-driven visual navigation is essential for many applications in robotics, and it has gained increasing interest in recent years. In this work, inspired by animal cognitive mechanisms, we propose a novel navigation architecture that simultaneously learns exploration policy and encodes environmental structure. First, to learn exploration policy directly from raw visual input, we use deep reinforcement learning as the basic framework and allow agents to create rewards for themselves as learning signals. In our approach, the reward for the current observation is driven by curiosity and calculated by a count-based approach and temporal distance. While agents learn exploration policy, we use temporal distance to find waypoints in observation sequences and incrementally describe the structure of the environment in a way that integrates episodic memory. Finally, space topological cognition is integrated into the model as a path planning module and combined with a locomotion network to obtain a more generalized approach to navigation. We test our approach in the DMlab, a visually rich 3D environment, and validate its exploration efficiency and navigation performance through extensive experiments. The experimental results show that our approach can explore and encode the environment more efficiently and has better capability in dealing with stochastic objects. In navigation tasks, agents can use space topological cognition to effectively reach the target and guide detour behaviour when a path is unavailable, exhibiting good environmental adaptability.