A few-shot learning-based reward estimation for mapless navigation of mobile robots using a Siamese Convolutional Neural Network

Abstract

Deep reinforcement learning-based approaches to mapless navigation have relied on the distance to the goal state being known a priori or that the distance to the goal can be obtained at each timestep. In artificial or simulated environments, obtaining the distance to the goal is considered a trivial task. Still, when applied to a real-world scenario, the distance must be obtained through complex localization techniques, and the use of localization techniques increases the complexity of the agent design. However, for agents navigating in unknown environments, using information about the goal to either form part of the state representation or act as the reward mechanism is usually expensive for both the robot design and for computing costs. This paper proposes using a pre-trained Siamese convolutional neural network (SCNN) to estimate the distance between an agent and its goal, thus enabling agents equipped with onboard cameras to navigate an unknown environment without needing localization sensors. This technique can be applied to environments where a goal location may be unknown, and the only information regarding the goal maybe a description of the goal state. Our experiments show that the Siamese network can learn the distance between the agent and its goal using relatively few training samples. Therefore, it is useful for mapless navigation using only visual state information and reduces the need for complex localization techniques