Improving Target-driven Visual Navigation with Attention on 3D Spatial Relationships

TL;DR

This paper enhances target-driven visual navigation in 3D environments by integrating attention on 3D knowledge graphs and a target skill extension module within deep reinforcement learning, improving efficiency, obstacle avoidance, and generalization.

Contribution

It introduces a novel approach combining 3D spatial knowledge graphs and sub-target learning to improve navigation performance and generalization in embodied AI tasks.

Findings

Outperforms baseline methods in SR and SPL metrics

Improves generalization across unseen targets and scenes

Effectively incorporates 3D spatial relationships and sub-targets

Abstract

Embodied artificial intelligence (AI) tasks shift from tasks focusing on internet images to active settings involving embodied agents that perceive and act within 3D environments. In this paper, we investigate the target-driven visual navigation using deep reinforcement learning (DRL) in 3D indoor scenes, whose navigation task aims to train an agent that can intelligently make a series of decisions to arrive at a pre-specified target location from any possible starting positions only based on egocentric views. However, most navigation methods currently struggle against several challenging problems, such as data efficiency, automatic obstacle avoidance, and generalization. Generalization problem means that agent does not have the ability to transfer navigation skills learned from previous experience to unseen targets and scenes. To address these issues, we incorporate two designs into classic DRL framework: attention on 3D knowledge graph (KG) and target skill extension (TSE) module. On the one hand, our proposed method combines visual features and 3D spatial representations to learn navigation policy. On the other hand, TSE module is used to generate sub-targets which allow agent to learn from failures. Specifically, our 3D spatial relationships are encoded through recently popular graph convolutional network (GCN). Considering the real world settings, our work also considers open action and adds actionable targets into conventional navigation situations. Those more difficult settings are applied to test whether DRL agent really understand its task, navigating environment, and can carry out reasoning. Our experiments, performed in the AI2-THOR, show that our model outperforms the baselines in both SR and SPL metrics, and improves generalization ability across targets and scenes.