arXiv:2606.16902v1 Announce Type: cross Abstract: This work addresses spatial question answering for service robots traversing long egocentric routes. Given a query such as "where can I find a dry cleaner on the way back home?", the system returns a metric coordinate that downstream navigation components can act on. Prior Spatial Question Answering approaches leverage retrieval-augmented agents built on closed-source models such as GPT-4o for path exploration. However, robots operating in the real world often cannot reliably depend on online closed-source models due to network instability, com
This work is published amid increasing recognition of the vulnerabilities and dependencies associated with relying on centralized, closed-source AI models for critical applications like robotics.
It demonstrates a pathway for robust, decentralized AI systems in robotics, reducing reliance on external cloud-based services and proprietary models, which is crucial for real-world deployment.
The paradigm shifts from continuous online reliance on closed-source models to more durable, on-robot binary execution and open-source models for spatial understanding and navigation.
- · Robotics manufacturers
- · Edge AI hardware providers
- · Open-source AI developers
- · Defence sectors
- · Proprietary cloud AI service providers
- · Centralized AI model developers
Robots gain increased autonomy and reliability in environments with unstable or no network connectivity.
Reduced operational costs for robot deployment as reliance on continuous, often costly, API calls to cloud models decreases.
Accelerated development of specialized, robust AI systems for specific robotic applications, potentially bypassing general-purpose models.
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