# AINav: Large Language Model-Based Adaptive Interactive Navigation

arXiv: https://arxiv.org/abs/2503.22942 · HTML: https://mlsanity.com/paper/2503.22942/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 19 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning](https://mlsanity.com/paper/2403.11552/) (2024)
- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [Tree-Planner: Efficient Close-loop Task Planning with Large Language Models](https://mlsanity.com/paper/2310.08582/) (2023)
- [Creative Robot Tool Use with Large Language Models](https://mlsanity.com/paper/2310.13065/) (2023)
- [Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models](https://mlsanity.com/paper/2308.15143/) (2023)
- [Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models](https://mlsanity.com/paper/2404.05291/) (2024)
- [LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees](https://mlsanity.com/paper/2404.05134/) (2024)
- [ArtPlanner: Robust Legged Robot Navigation in the Field](https://mlsanity.com/paper/2303.01420/) (2023)
- [Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models](https://mlsanity.com/paper/2310.08873/) (2023)
- [Visibility-Aware Navigation Among Movable Obstacles](https://mlsanity.com/paper/2212.02671/) (2022)

## Built on by (most cited first, 2 of 2)

- [Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym](https://mlsanity.com/paper/2604.09338/) (2026)
- [Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots](https://mlsanity.com/paper/2610.08812/) (2026)

## Abstract (arXiv)

Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive interactive navigation approach that proactively interacts with environments to create feasible paths to achieve originally unreachable goals. Specifically, we present a primitive skill tree for task planning with large language models (LLMs), facilitating effective reasoning to determine interaction objects and sequences. To ensure robust subtask execution, we adopt reinforcement learning to pre-train a comprehensive skill library containing versatile locomotion and interaction behaviors for motion planning. Furthermore, we introduce an adaptive replanning approach featuring two LLM-based modules: an advisor serving as a flexible replanning trigger and an arborist for autonomous plan adjustment. Integrated with the tree structure, the replanning mechanism allows for convenient node addition and pruning, enabling rapid plan adaptation in a priori unknown environments. Comprehensive simulations and experiments have demonstrated AINav's effectiveness and adaptivity in diverse scenarios. The supplementary video is available at: https://youtu.be/CjXm5KFx9AI.