Lifelong Embodied Navigation Learning
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 k...
| Authors | Xudong Wang, Jiahua Dong, Baichen Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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