Learning to Predict the Future from Monocular Vision for Efficient Human-Aware Navigation

Abstract

Human-aware navigation (HAN) aims to build autonomous agents that robustly and naturally navigate in human-centered environments. Due to the complex and dynamic nature of this task, existing approaches typically rely on sophisticated pipelines that separately process perception and decision-making to solve it. In this work, we propose an Obstruction Distance Vector based End-to-End Model (ODVEEM), using monocular vision for navigation around humans. The Obstruction Distance Vector (ODV) is an intermediate representation in our model, leveraged to describe the Obstruction Distance to the first future collision in all possible directions in the horizontal field of view. As ODV cannot be calculated directly in the real world, we design a neural network for ODV estimation, formulating it as a classification problem with auxiliary proxy tasks, which play a key role in effectively predicting the implicit future motion of nearby humans. Taking advantage of ODV, ODVEEM supervised by human behavioral heuristics is employed to guide the agent to reach a goal efficiently and avoid potential collisions. Several challenging experiments show our method's substantial improvement over a number of baseline methods, attaining solid performance with zero-shot transfer to unseen simulated and real-world environments.