Calibrating perceptual trust in robotic vision: The Metacognition-based Adaptive Scanning System (MASS)

The current paradigm in robotic vision for manipulation is characterized by a fundamental efficiency-accuracy trade-off between low-cost 2D scanning and resource-intensive 3D reconstruction. While constant 3D scanning provides geometric precision, its indiscriminate application leads to excessive computational overhead and temporal delays. Conversely, relying on static 2D perspectives often fails to resolve geometric ambiguities, resulting in grasping failures. To address this dilemma, we propose a Metacognition-based Adaptive Scanning System (MASS) framework. This perspective explores a self-evaluative “Metacognitive Loop” that mimics human cognitive monitoring by quantifying epistemic uncertainty in initial visual data. By dynamically transitioning from 2D to 3D scanning only when information deficiency is detected, the system achieves an optimized balance between perceptual reliability and operational efficiency. We analyze this framework through various scenarios of geometric ambiguity, such as altitude deception and orthographic inconsistency, to demonstrate the necessity of cognitive awareness in autonomous systems. This approach suggests a new direction for resource-efficient, intelligent robotics in safety-critical and dynamic environments.