Lifelong Embodied Navigation Learning
This work is significant for researchers developing embodied AI agents, as it tackles the critical problem of lifelong learning and catastrophic forgetting in navigation tasks, aiming to create more adaptable and universal agents.
The paper addresses the challenge of catastrophic forgetting in embodied navigation agents when continually acquiring new navigation skills across diverse tasks and instruction styles. They propose Uni-Walker, a framework that decouples navigation knowledge into shared and specific components, using strategies like knowledge inheritance and expert co-activation for shared knowledge, and expert subspace orthogonality and chain-of-thought reasoning for specific knowledge.
Embodied navigation agents powered by large language models have shown strong performance on individual tasks but struggle to continually acquire new navigation skills, which suffer from catastrophic forgetting. We formalize this challenge as lifelong embodied navigation learning (LENL), where an agent is required to adapt to a sequence of navigation tasks spanning multiple scenes and diverse user instruction styles, while retaining previously learned knowledge. To tackle this problem, we propose Uni-Walker, a lifelong embodied navigation framework that decouples navigation knowledge into task-shared and task-specific components with Decoder Extension LoRA (DE-LoRA). To learn the shared knowledge, we design a knowledge inheritance strategy and an experts co-activation strategy to facilitate shared knowledge transfer and refinement across multiple navigation tasks. To learn the specific knowledge, we propose an expert subspace orthogonality constraint together and a navigation-specific chain-of-thought reasoning mechanism to capture specific knowledge and enhance instruction-style understanding. Extensive experiments demonstrate the superiority of Uni-Walker for building universal navigation agents with lifelong learning.