SuperNav: An Agentic Navigation System for Any Task in Any Scene

arXiv:2610.12126 · cs.RO, cs.CV · Submitted 2026-10-08 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "SuperNav: An Agentic Navigation System for Any Task in Any Scene".

Dev: The gist The SuperNav framework equips a pretrained MLLM with specialized agent harness components, including Navigation Skills, agent-oriented Tools,

Rosa: First, who's behind it and why it matters.

Paper summary: Rosa: So, SuperNav is essentially this system that lets a pretrained multimodal model take any task in any scene without needing specific navigation training for every single new thing you want it to do.

Dev: Yeah, the title points right to that idea; they're building this harness around existing large multimodal models instead of training entirely new ones from scratch.

Taro: They’re giving a general model a set of skills and tools so it can navigate without needing those specialized fine-tuning sessions we used to have for every new navigation task.

Rosa: It means we're thinking about how these massive models can handle tasks they haven't been explicitly trained for, just by giving them the right procedural guidance and execution tools.

Dev: From an engineering standpoint, it’s about creating this loop where the model decides what to do and then uses tools to execute that move, which helps manage latency and failure modes in a more controlled way.

Taro: What really stands out is how they integrate navigation skills—those procedural hints for searching or recovering—into the model's reasoning process on demand. It’s not just one big program; it’s modular guidance.

Rosa: And when you look at the performance numbers, they show this system works across several types of tasks, from single-object navigation to more complex, demand-driven scenarios.

Dev: But the caveats are important too; the paper points out that its success heavily depends on how well the underlying multimodal model already understands things semantically.

Taro: And there’s another thing they flag—the geometric execution part relies on having a good map or scene geometry available for the robot to use.

Rosa: So, what this changes for us is seeing a way to build navigation capabilities around the general reasoning power of an AI instead of needing entirely new models for every single application.

Dev: It’s about making these general-purpose models much more useful in real environments by giving them a standardized way to interact with physical space.

Taro: We've got some good examples from their testing, but the next thing we need to look at is how this framework holds up when things get really messy or unpredictable outside of those clean benchmarks.

Conclusion: Rosa: So we've covered how SuperNav gives a pretrained multimodal model a harness so it can navigate any task in any scene without needing specific navigation training for every new thing you want it to do.

Dev: Yeah, the title points right to that idea; they're building this harness around existing large multimodal models instead of training entirely new ones from scratch.

Taro: They’re giving a general model a set of skills and tools so it can navigate without needing those specialized fine-tuning sessions we used to have for every new navigation task.

Rosa: It means we're thinking about how these massive models can handle tasks they haven't been explicitly trained for, just by giving them the right procedural guidance and execution tools.

Dev: From an engineering standpoint, it’s about creating this loop where the model decides what to do and then uses tools to execute that move, which helps manage latency and failure modes in a more controlled way.

Taro: What really stands out is how they integrate navigation skills—those procedural hints for searching or recovering—into the model's reasoning process on demand. It’s not just one big program; it’s modular guidance.

Rosa: And when you look at the performance numbers, they show this system works across several types of tasks, from single-object navigation to more complex, demand-driven scenarios.

Dev: But the caveats are important too; the paper points out that its success heavily depends on how well the underlying multimodal model already understands things semantically.

Taro: And there’s another thing they flag—the geometric execution part relies on having a good map or scene geometry available for the robot to use.

Rosa: So, what this changes for us is seeing a way to build navigation capabilities around the general reasoning power of an AI instead of needing entirely new models for every single application.

Dev: It’s about making these general-purpose models much more useful in real environments by giving them a standardized way to interact with physical space.

Taro: We've got some good examples from their testing, but the next thing we need to look at is how this framework holds up when things get really messy or unpredictable outside of those clean benchmarks.

Jinkai Zhang, Jingyi Xu, Yuanhong Yu, Jiarui Guo

Zhejiang University · Shenzhen University · Causa Robotics

cs.RO, cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: 20 pages, 7 figures. Project page: https://zju3dv.github.io/SuperNav/

Project page: https://zju3dv.github.io/SuperNav/1

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 80/100

The gist: The gist The SuperNav framework equips a pretrained MLLM with specialized agent harness components, including Navigation Skills, agent-oriented Tools, and task-progress management to enable sustained

Key concepts

Agent Loop
This is the core operational cycle where the MLLM reasons about a request or scene. The model decides whether to observe, move toward a goal, update its current state, or consult navigation skills. This loop allows the MLLM to adapt its navigation strategy dynamically based on real-time visual observations and execution feedback.
Agent-oriented Tools
These are functions that bridge the MLLM's high-level decisions with physical actions in the environment. They include tools for initialization, turning, and goal progress management. The system uses a unified observation–motion interface where destinations specified visually can be revised based on feedback from the execution.
Navigation Skills
These are reusable procedural guidance modules that provide context to the MLLM's decisions. They offer advice on recurring navigation challenges, such as where to search or how to recover after a failed movement. These skills guide decision-making without adding new environment capabilities themselves.
Interchangeable Motion Backend
This design separates the choice of destination from the actual physical execution of motion. The system can use different methods for movement, such as one based on geometric maps or another learned method predicting local displacement increments. This flexibility allows the MLLM to focus on selecting where to go while supporting various underlying motion systems.

Terminology

Summary

The gist The SuperNav framework equips a pretrained MLLM with specialized agent harness components, including Navigation Skills, agent-oriented Tools, and task-progress management to enable sustained navigation across diverse tasks and scenes without navigation-specific fine-tuning.

How it works

SuperNav is designed around an Agent loop that coordinates the MLLM's reasoning with tool execution and skill guidance. The MLLM interprets requests and visual observations to choose an operation from observe, move, update goal state, or read relevant Skill guidance. This loop allows the MLLM to adapt its use of navigation capabilities to the current request and scene without navigation-specific fine-tuning.

Agent-oriented Tools

The harness provides agent-oriented Tools that connect the MLLM’s decisions to executable environment operations. These tools include initialization, observation, turning, visual-point navigation, goal-progress management, and termination. The central observation–motion interface implements these principles by pairing visual destinations with updated observations and execution feedback. This unified visual-point interface allows the model to specify destinations directly in images and revise its decisions from execution feedback.

Navigation Skills

Navigation Skills organize reusable navigation experience into procedural guidance that the MLLM can read on demand. These skills guide three recurring decisions: where to search, how to recover, and when to finish. For example, recovery guidance recommends changing the point or route after failed motion. These skills influence decisions by entering the context but do not add environment capabilities nor programmatically enforce action sequences or semantic judgments.

Evaluation and Performance

SuperNav outperforms four evaluated baselines on instance-level, multi-object, and demand-driven tasks. Category-level evaluation on HM3D and deployment on a real quadruped robot further demonstrate its applicability across environments. For instance, SuperNav achieves 78.00% success on single-object navigation and 59.50% on demand-driven navigation. The system's performance is measured using metrics like Success Rate (SR) and Success Weighted by Path Length (SPL).

Key Design Choices

The system employs an interchangeable motion backend, separating destination selection from the execution mechanism. The geometric backend uses depth and navigation-mesh geometry for Geo-based Executor, while the learned backend predicts local displacement increments based on a marked reference image. The Learned Executor architecture fuses visual representation and age embeddings to predict displacement increments through a conditional flow prediction. This design allows the MLLM to focus on selecting destinations while supporting different motion backends through the same interaction pattern.

Real-World Application

On a Unitree Go2 with four-view RGB and LiDAR-based geometric execution, SuperNav revises routes at blocked passages, verifies targets, and transitions between goals. This demonstrates the framework's applicability across task and scene settings. The limitations acknowledge that SuperNav relies on the underlying MLLM for semantic judgments and is therefore limited by its capabilities. This suggests that while powerful, the system's reliability depends heavily on the MLLM's reasoning ability.

Conclusion

SuperNav presents a navigation harness that enables a pretrained MLLM to interpret requests, understand scenes, and make decisions while delegating motion execution to tools without navigation-specific fine-tuning. The framework is demonstrated across category-level, instance-level, multi-object, and demanddriven navigation with real-robot deployment. This work supports building navigation capabilities around a general-purpose MLLM’s existing understanding and reasoning. The limitations note that geometric execution relies on available maps or scene geometry, while MLLM inference incurs computational cost and substantial latency variability. The paper concludes that the framework supports sustained interaction across different task requirements and environments.

Limitations

The limitations specify that SuperNav relies on the underlying MLLM for semantic judgments and is therefore limited by its capabilities. Geometric execution relies on available maps or scene geometry. MLLM inference incurs computational cost and substantial latency variability, making task duration less predictable <ref:2610.

Improvements for AI systems

  1. The improved AI system can sustain navigation across different task requirements and environments by delegating motion execution to specialized tools, allowing the MLLM to focus on interpreting requests, understanding scenes, and making decisions while preserving its general-purpose capabilities.

  2. The system gains enhanced adaptability because it uses Navigation Skills for procedural guidance that the MLLM can read on demand, enabling the agent to perform specific actions like Search, Recheck, or Recover when encountering obstacles.

  3. The system achieves higher success rates across diverse navigation challenges by utilizing a unified visual-point interface where the MLLM specifies a destination directly in an observation image, and the navigation tool executes the movement and returns feedback for the next decision.

  4. The improved system can handle complex, multi-stage requests by maintaining task-progress records and managing interaction context, allowing it to continue reasoning across successive actions even when decisions are made over a long horizon.

  5. The system's performance is boosted by leveraging interchangeable motion backends, as it supports both a Geo-based Executor and a Learned Executor, enabling the model to choose the most appropriate execution mechanism for the current scene or task.

Sources

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