C-NAV: Towards Self-Evolving Continual Object Navigation in Open World

TL;DR

C-Nav introduces a continual object navigation framework with anti-forgetting mechanisms and adaptive sampling, enabling agents to adapt to new categories in open-world environments while retaining prior knowledge.

Contribution

The paper presents a novel continual visual navigation framework that effectively mitigates catastrophic forgetting and reduces memory usage in dynamic environments.

Findings

C-Nav outperforms existing methods across multiple architectures.

It achieves superior performance with lower memory requirements.

The adaptive sampling strategy enhances experience diversity and efficiency.

Abstract

Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of object categories during training, overlooking the real-world requirement for continual adaptation to evolving scenarios. To facilitate related studies, we introduce the continual object navigation benchmark, which requires agents to acquire navigation skills for new object categories while avoiding catastrophic forgetting of previously learned knowledge. To tackle this challenge, we propose C-Nav, a continual visual navigation framework that integrates two key innovations: (1) A dual-path anti-forgetting mechanism, which comprises feature distillation that aligns multi-modal inputs into a consistent representation space to ensure representation consistency, and feature replay that retains temporal features within the action decoder to ensure policy consistency. (2) An adaptive sampling strategy that selects diverse and informative experiences, thereby reducing redundancy and minimizing memory overhead. Extensive experiments across multiple model architectures demonstrate that C-Nav consistently outperforms existing approaches, achieving superior performance even compared to baselines with full trajectory retention, while significantly lowering memory requirements. The code will be publicly available at https://bigtree765.github.io/C-Nav-project.