Co-GLANCE: New System Enables Robot Teams to Resolve Visual Uncertainty in Real-Time
Researchers have introduced Co-GLANCE, an onboard perception and decision-making system that allows heterogeneous robot teams to detect and resolve perceptual uncertainty in real time without relying on cloud-based inference. The system distills vision-language model capabilities into a compact end-to-end model and uses conformal prediction to provide statistically valid uncertainty guarantees, triggering active perception by dispatching the most suitable robot to resolve ambiguous viewpoints. The work addresses a key bottleneck in multi-robot deployment—balancing semantic reasoning quality with the latency and connectivity constraints of real-world outdoor operation.
Co-GLANCE is a newly proposed system for heterogeneous robot teams—such as air-ground pairings—that must operate in unstructured outdoor environments where occlusions and limited viewpoints create perceptual uncertainty. Rather than relying on large vision-language models running in the cloud, Co-GLANCE distills their semantic reasoning into a lightweight onboard model capable of occlusion segmentation and robot allocation simultaneously. To quantify uncertainty rigorously, the system combines conformal prediction with selective abstention, yielding statistically valid coverage guarantees across segmentation, allocation, and detection outputs. These calibrated uncertainty estimates directly drive active perception: the system automatically dispatches the most appropriate robot to acquire informative viewpoints and resolve ambiguity. In real-world evaluations, Co-GLANCE outperformed cloud-based vision-language model baselines by 25% on occlusion segmentation accuracy and 36% on robot allocation accuracy, while reducing per-frame inference latency by a factor of 350. The authors also release an air-ground dataset and accompanying code and videos to support future research in this area.
What's missing
The distillation process from the teacher vision-language model may introduce performance ceilings tied to the teacher's own limitations, and long-term robustness under sensor degradation or adversarial conditions is not addressed. The statistical guarantees from conformal prediction hold only under the assumption that calibration and deployment data are exchangeable, a condition that may not hold in highly dynamic or novel environments.
What different sources said
- arXiv cs.LGCenter
Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
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