机器人在感知扭曲下仍能自发构建导航结构
Perceptual Distortions and Autonomous Representation Learning in a Minimal Robotic System
- 用模拟两轮机器人探索环境，分析传感器数据
- 发现感知空间虽扭曲，却仍保留物理环境的结构特征
- 适合研究具身智能与自主导航的入门者
自主代理，尤其是机器人领域，依赖感官信息来感知和导航环境。然而，这些感官输入往往不完美，导致代理对世界的内部表征出现扭曲。本文通过一个极简机器人系统，研究了这种感知扭曲的本质及其对自主表征学习的影响。我们使用一个配备距离传感器和指南针的模拟两轮机器人，在简单的方形环境中进行随机探索。通过对传感器数据的分析，我们展示了扭曲的感知空间如何形成。尽管存在扭曲，我们仍发现了感知空间中与物理环境相关的涌现结构，揭示了机器人在无显式空间信息的情况下，如何自主学习用于导航的结构化表征。这项工作有助于理解具身认知、最小化代理以及感知在自生成导航策略中的作用。
原文摘要 · Abstract (English)
Autonomous agents, particularly in the field of robotics, rely on sensory information to perceive and navigate their environment. However, these sensory inputs are often imperfect, leading to distortions in the agent's internal representation of the world. This paper investigates the nature of these perceptual distortions and how they influence autonomous representation learning using a minimal robotic system. We utilize a simulated two-wheeled robot equipped with distance sensors and a compass, operating within a simple square environment. Through analysis of the robot's sensor data during random exploration, we demonstrate how a distorted perceptual space emerges. Despite these distortions, we identify emergent structures within the perceptual space that correlate with the physical environment, revealing how the robot autonomously learns a structured representation for navigation without explicit spatial information. This work contributes to the understanding of embodied cognition, minimal agency, and the role of perception in self-generated navigation strategies in artificial life.
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