Efficient and Generalizable Environmental Understanding for Visual Navigation

TL;DR

This paper introduces Causality-Aware Navigation (CAN), a novel approach that incorporates causal reasoning to improve environmental understanding in visual navigation tasks, leading to better performance across diverse settings.

Contribution

The paper presents a causal framework for visual navigation and proposes CAN, which effectively integrates causal understanding to enhance generalization and performance.

Findings

CAN outperforms baselines across various tasks and environments.

The Causal Understanding Module generalizes well in reinforcement and supervised learning.

The approach does not add computational overhead.

Abstract

Visual Navigation is a core task in Embodied AI, enabling agents to navigate complex environments toward given objectives. Across diverse settings within Navigation tasks, many necessitate the modelling of sequential data accumulated from preceding time steps. While existing methods perform well, they typically process all historical observations simultaneously, overlooking the internal association structure within the data, which may limit the potential for further improvements in task performance. We address this by examining the unique characteristics of Navigation tasks through the lens of causality, introducing a causal framework to highlight the limitations of conventional sequential methods. Leveraging this insight, we propose Causality-Aware Navigation (CAN), which incorporates a Causal Understanding Module to enhance the agent's environmental understanding capability. Empirical evaluations show that our approach consistently outperforms baselines across various tasks and simulation environments. Extensive ablations studies attribute these gains to the Causal Understanding Module, which generalizes effectively in both Reinforcement and Supervised Learning settings without computational overhead.