LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory

2026-09-02

AI Summary

为解决VLN中数据需求大和计算存储开销高的问题，提出LookStep框架，通过语言中心的未来状态建模和事件驱动的滚动记忆来提高导航效率。

Abstract

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 requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose LookStep, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7\% success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at https://github.com/kunyang-YU/LookStep.

Problem

• Vision-Language Navigation
• Efficiency
• Resource Efficiency
• Data Usage
• Memory Overhead

Innovation

• Language Centric Future State Modeling
• Event Driven Rolling Memory
• Vision-Language Navigation
• Efficient Resource Utilization