ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models
arXiv:2606.30696v1 Announce Type: cross Abstract: Enabling robots to follow natural language commands to complete zero-shot long-horizon tasks remains challenging. It requires extracting implicit temporal and logical constraints from natural language commands and executing multiple sub-tasks accordingly. Recent zero-shot object navigation methods use vision-language models (VLMs) to guide frontier-based exploration in unknown environments, but they are limited to single-target tasks. Real-world commands such as "Clean either the chair or the couch, then turn on the tv." require navigating to m
The proliferation of advanced vision-language models makes it feasible to tackle complex real-world robot navigation challenges that were previously intractable.
This research addresses a key limitation in robotics by enabling zero-shot, long-horizon tasks, which is crucial for deploying robots in dynamic, unstructured environments without extensive pre-programming.
Robots can now interpret and execute more nuanced natural language commands involving temporal and logical constraints, moving beyond single-target navigation.
- · Robotics companies
- · Logistics and automation sectors
- · AI model developers
- · Home robotics
- · Companies relying on highly structured and pre-programmed robotic tasks
- · Manual labor in repetitive navigation-centric roles
Improved capabilities for autonomous robots to perform complex tasks in novel environments.
Accelerated development and adoption of general-purpose robots in various industries and consumer settings.
Potential for new service economies built around customizable and adaptable robotic agents.
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Read at arXiv cs.CL