Target-driven Indoor Visual Navigation Using Inverse Reinforcement Learning

Deep reinforcement learning has greatly simplified visual navigation by utilizing the end-to-end network training strategy. Unlike previous navigation methods which build upon high-precision maps, deep reinforcement learning-based method enables real-time navigation by only taking one image as input at a time. As such, deep reinforcement learning based navigation methods are applicable to a variety number of applications in robotics/vision communities, thanks to its light-weight computational cost. Despite the advantages, however, these methods still suffer from inefficient data exploration and poor convergence on network training. In this paper, we propose to use inverse reinforcement learning to solve the problem,which can provide more accurate and efficient guidance for decision-making. The proposed method is able to learn a more effective reward function from less training data. Experiments demonstrated that the proposed method achieves a higher success rate of navigation and produces paths that are more similar to the optimal ones compared to the reinforcement learning baselines.