Evidence of an Emergent "Self" in Continual Robot Learning

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 knowledge and 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 robot is subjected to 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. We suggest that this principle can offer a window into exploring selfhood in other cognitive AI systems.

Figures (17)

• Figure 1: Continual learning produces a stable self-like core. Compared to a single-task baseline, multi-behavior training yields a subnetwork that remains stable across behaviors ("persistent self"), while other components vary. Representative results from the first hidden layer of each network shown.
• Figure 2: Visualization of the persistent self in static and variable conditions. Each policy is shown on its own plane, with hidden-layer units ordered by co-activation-based subnetworks, showing how size and structure of the subnetworks across learning. Alluvial flows connect matched neuron families across successive policies, grouped by source subnetwork $\rightarrow$ target subnetwork; dark purple denotes the self subnetwork. Flow width indicates how many matched families pass between subnetworks, and opacity encodes mean persistence score. Left: three consecutively trained walk policies produce a more fragmented organization, with flows distributed across many groupings. Right: walk$\rightarrow$wiggle$\rightarrow$bob reveals one dominant subnetwork that remains continuous across policies, while smaller groupings split, merge, and reroute more strongly.
• Figure 3: Quantitative evidence for a persistent self-like subnetwork. Shown here is one trained policy from each condition (constant-task and continual). Although both policies successfully perform the same behavior, they exhibit markedly different internal structure. The top panel shows the reordered neuron--neuron co-activation matrix with inferred subnetwork boundaries, and the bottom panel shows per-neuron persistence score in the same ordering. For a run-level overlay of this view across many plateaued snapshots and additional examples, see \ref{['app:tessellation-and-examples']}.
• Figure 4: Subnetwork Persistence and Size: Constant-task baseline vs Continual Learning Mean persistence score (top) and self subnetwork size (bottom) across 50 cycles for both hidden layers. The continual-learning (multi-behavior) agent shows a clear separation between the self subnetwork (largest subnetwork) and the pooled task subnetwork (all remaining units), while the walk-only baseline exhibits weaker separation and comparatively small self-subnetwork sizes. Error bars indicate inter-quartile range (IQR); dashed lines denote across-cycle means.
• Figure 5: Reorganization concentrates in task-like regions at behavior switches. Overlay heatmaps show how much each subnetwork changes when learning a new behavior. The self-like subnetwork exhibits consistently smaller change than the pooled task-like region, validating the stable core alongside components that relearn more aggressively to acquire new skills.