Multimodal lifelong navigation requires an agent to autonomously explore unseen environments while sequentially completing navigation tasks specified by object categories, language descriptions, or reference images. Existing methods primarily accomplish these tasks by constructing state-centric semantic scene graphs. B...
Yang Chen, Zhen-Yu Huang, Wenbo Fu et al.· 2 citations
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requi...
Kun-Yang Yu, Ying-Zhe Li, Hongyu Xu et al.· 2 citations
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requi...
Kun-Yang Yu, Ying-Zhe Li, Hongming Xu et al.· 0 citations
This paper combines symbolic regression with LLMs for feature engineering (SymboLLM-FE) to solve the dual challenges of poor interpretability and numerous iterations by employing a statistical prior-grounded LLM refinement mechanism and single-digit LLM calls.
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou et al.· 0 citations
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