arXiv:2606.09919v1 Announce Type: new Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding. Perceptual uncertainty, arising from sources such as occlusions, manifests differently across robot viewpoints depending on scene structure. Detecting and resolving sources of perceptual uncertainty requires both scene-based contextual reasoning and capability-aware robot allocation. While vision-language models provide strong semantic priors for both, they are
The paper leverages recent advancements in vision-language models for robotic perception, which are becoming increasingly sophisticated for complex environmental understanding.
This research addresses a critical challenge for autonomous robot operation in dynamic, real-world environments, directly impacting the reliability and deployment of advanced robotic systems.
The proposed Co-GLANCE system introduces a method for heterogeneous robot teams to actively and collaboratively reduce perceptual uncertainty by intelligently allocating tasks based on scene context and individual robot capabilities.
- · Robotics industry
- · Defence contractors
- · Logistics and infrastructure
- · manual human inspection tasks
Heterogeneous robot teams achieve higher operational reliability and efficiency in complex outdoor environments.
Accelerated deployment of autonomous systems in sectors like defense, disaster response, and large-scale agriculture due to improved environmental understanding.
Enhanced data collection and mapping capabilities for AI models, creating a virtuous cycle of improved perception and autonomous system performance.
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Read at arXiv cs.LG