Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification
arXiv:2604.16993v2 Announce Type: replace Abstract: As embodied AI transitions to real-world deployment, the success of the Vision-and-Language Navigation (VLN) task tends to evolve from mere reachability to social compliance. However, current agents suffer from a "goal-driven trap", prioritizing physical geometry ("can I go?") over semantic rules ("may I go?"), frequently overlooking subtle regulatory constraints. To bridge this gap, we establish Rule-VLN, the first large-scale urban benchmark for rule-compliant navigation. Spanning a massive 29k-node environment, it injects 177 diverse regul
As embodied AI moves towards real-world application, the need for agents to adhere to social and regulatory norms is becoming critical, pushing research beyond mere physical reachability.
A strategic reader should care because the development of rule-compliant AI agents addresses a significant barrier to safe and ethical deployment in public spaces, impacting adoption and public acceptance.
AI navigation systems are shifting from purely geometric pathfinding to an integration of semantic reasoning and regulatory compliance, enabling more sophisticated and socially aware robotic operations.
- · Embodied AI developers
- · Robotics companies
- · Urban planning and smart city initiatives
- · Public safety and regulatory bodies
- · Companies with purely geometry-focused navigation systems
The new benchmark facilitates the development of AI agents capable of navigating complex urban environments while respecting human societal rules.
This advancement could accelerate the deployment of autonomous delivery robots, self-driving vehicles, and service robots in populated areas.
The ability of AI to understand and adhere to nuanced regulations might lead to broader societal trust in autonomous systems, influencing future policy and ethical frameworks.
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Read at arXiv cs.AI