VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated and Steerable Navigation

TL;DR

VAMOS introduces a hierarchical vision-language-action model that separates semantic planning from embodiment constraints, enabling versatile, safe, and steerable robot navigation across diverse environments and robot types.

Contribution

The paper presents a novel hierarchical VLA model that decouples planning from embodiment grounding, allowing cross-embodied navigation and improved safety in real-world robot navigation.

Findings

Higher success rates in indoor and outdoor navigation.

Effective cross-embodied navigation across different robot types.

3X higher success rates by rejecting infeasible plans.

Abstract

A fundamental challenge in robot navigation lies in learning policies that generalize across diverse environments while conforming to the unique physical constraints and capabilities of a specific embodiment (e.g., quadrupeds can walk up stairs, but rovers cannot). We propose VAMOS, a hierarchical VLA that decouples semantic planning from embodiment grounding: a generalist planner learns from diverse, open-world data, while a specialist affordance model learns the robot's physical constraints and capabilities in safe, low-cost simulation. We enabled this separation by carefully designing an interface that lets a high-level planner propose candidate paths directly in image space that the affordance model then evaluates and re-ranks. Our real-world experiments show that VAMOS achieves higher success rates in both indoor and complex outdoor navigation than state-of-the-art model-based and end-to-end learning methods. We also show that our hierarchical design enables cross-embodied navigation across legged and wheeled robots and is easily steerable using natural language. Real-world ablations confirm that the specialist model is key to embodiment grounding, enabling a single high-level planner to be deployed across physically distinct wheeled and legged robots. Finally, this model significantly enhances single-robot reliability, achieving 3X higher success rates by rejecting physically infeasible plans. Website: https://vamos-vla.github.io/