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This study provides empirical evidence that robots engaging in continual learning develop a stable neural subnetwork distinct from rapidly acquired skills, offering a quantifiable marker for an emergent "self." The research demonstrates that this invariant structure is functionally critical, as preserving it enhances adaptation while damaging it degrades performance across diverse robotic platforms. By linking cognitive stability to adaptive capability, the work advances our understanding of how persistent identity-like structures might arise in artificial agents without explicit programming.
Evidence of an Emergent "Self" in Continual Robot Learning
arXiv:2603.24350v3 Announce Type: replace-cross Abstract: A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a "self", and if so how to differentiate the "self" from other cognitive structures. We propose that the "self" can be isolated by seeking the invariant portion of cognitive process that changes relatively little compared to more rapidly acquired cognitive skills - because our self is the most persistent aspect of our experiences. We used this principle to analyze the cognitive structure of robots under two conditions: One robot learns a constant task, while a second undergoes continual learning under variable tasks. We find that robots subjected to continual learning develop an invariant subnetwork that is significantly more stable (p < 0.001) compared to the control, and that this subnetwork is also functionally important: preserving it aids adaptation while damaging it impairs performance. We validate this pattern across three different robots spanning locomotion and manipulation.
Abstract:A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a "self", and if so how to differentiate the "self" from other cognitive structures. We propose that the "self" can be isolated by seeking the invariant portion of cognitive process that changes relatively little compared to more rapidly acquired cognitive skills - because our self is the most persistent aspect of our experiences. We used this principle to analyze the cognitive structure of robots under two conditions: One robot learns a constant task, while a second undergoes continual learning under variable tasks. We find that robots subjected to continual learning develop an invariant subnetwork that is significantly more stable (p < 0.001) compared to the control, and that this subnetwork is also functionally important: preserving it aids adaptation while damaging it impairs performance. We validate this pattern across three different robots spanning locomotion and manipulation.
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2603.24350 [cs.RO]
(or arXiv:2603.24350v3 [cs.RO] for this version)
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