发现持续学习机器人会自发形成稳定的‘自我’认知模块。
Evidence of an Emergent "Self" in Continual Robot Learning
- 通过识别认知中变化最小的稳定部分，定位‘自我’结构。
- 持续学习机器人出现显著更稳定的子网络（p<0.001）。
- 该模块对适应新任务至关重要，适合研究智能体自指机制者阅读。
理解自我意识的核心挑战在于如何量化智能系统是否具备‘自我’概念，以及如何区分‘自我’与其他认知结构。我们提出，‘自我’可通过寻找认知过程中相对不变的部分来识别——因为我们的自我是经验中最持久的成分。我们以此原则分析了两种条件下机器人的认知结构：一个机器人学习固定任务，另一个在可变任务下持续学习。结果显示，持续学习的机器人发展出一个显著更稳定的子网络（p < 0.001），且该子网络功能关键：保留它有助于适应新任务，破坏它则损害性能。该模式在三种不同机器人（涵盖运动与操作任务）中均得到验证。
原文摘要 · Abstract (English)
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.
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