Multimodal fusion for autonomous navigation via deep reinforcement learning with sparse rewards and hindsight experience replay

The multimodal perception of intelligent robots is essential for achieving collision-free and efficient navigation. Autonomous navigation is enormously challenging when perception is acquired using only vision or LiDAR sensor data due to the lack of complementary information from different sensors. This paper proposes a simple yet efficient deep reinforcement learning (DRL) with sparse rewards and hindsight experience replay (HER) to achieve multimodal navigation. By adopting the depth images and pseudo-LiDAR data generated by an RGB-D camera as input, a multimodal fusion scheme is used to enhance the perception of the surrounding environment compared to using a single sensor. To alleviate the misleading way for the agent to navigate with dense rewards, the sparse rewards are intended to identify its tasks. Additionally, the HER technique is introduced to address the sparse reward navigation issue for accelerating optimal policy learning. The results show that the proposed model achieves state-of-the-art performance in terms of success, crash, and timeout rates, as well as generalization capability.