AgenticNav: Zero-Shot Vision-and-Language Navigation as a Tool-Calling Harness
Yijian Li, Changze Li, Hantian Shi, Jiaying Luo, Jiyuan Cai, Ming Yang, Tong Qin
THE PROBLEM
This paper focuses on vision language learning. This paper shows how to make a Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. navigate indoor environments using natural language instructions with zero real-world Robot LearningTrainingThe process of fitting a model using data or experience., by treating Modern Robot LearningVision-Language Model (VLM)A model that understands both images and text. reasoning as a tool-calling interface where the model directly selects target pixels and queries depth on-demand rather than being constrained to predefined waypoints. Real-world experiments validate that this approach generalizes better than waypoint-based alternatives while maintaining compact memory footprints. Read the paper by tracking the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. definition, the Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or data assumptions, and the evidence that supports the claimed improvement.
HOW IT WORKS
Task framing
Core method
Data and supervision
Evaluation evidence
KEY RESULTS
This paper shows how to make a Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. navigate indoor environments using natural language instructions with zero real-world Robot LearningTrainingThe process of fitting a model using data or experience., by treating Modern Robot LearningVision-Language Model (VLM)A model that understands both images and text. reasoning as a tool-calling interface where the model directly selects target pixels and queries depth on-demand rather than being constrained to predefined waypoints. Real-world experiments validate that this approach generalizes better than waypoint-based alternatives while maintaining compact memory footprints.
WHY DEVELOPERS SHOULD CARE
This paper shows how to make a Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. navigate indoor environments using natural language instructions with zero real-world Robot LearningTrainingThe process of fitting a model using data or experience., by treating Modern Robot LearningVision-Language Model (VLM)A model that understands both images and text. reasoning as a tool-calling interface where the model directly selects target pixels and queries depth on-demand rather than being constrained to predefined waypoints. Real-world experiments validate that this approach generalizes better than waypoint-based alternatives while maintaining compact memory footprints.
LIMITATIONS
The main limitation to check is whether the claimed behavior holds outside the paper's reported setup. That means testing across different Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. embodiments, scenes, objects, and data distributions.
WHAT COMES NEXT
The practical next step is independent reproduction with clear baselines, ablations, and stress tests. For a developer, the useful follow-up is to map the paper's vision language learning assumptions onto a concrete Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. stack, then test the smallest version of the method that could run end to end.