用类脑机器人模拟老鼠的空间关联学习能力。
Mimicking associative learning of rats via a neuromorphic robot in open field maze using spatial cell models
- 基于海马体空间细胞模型构建类脑导航系统。
- 实现在开放迷宫中的实时空间关联学习，无需大量数据训练。
- 适合资源受限场景如火星探测的智能机器人开发。
数据驱动的人工智能在各类认知任务中表现出色，但依赖大规模数据和神经网络导致功耗高、适应性差，尤其在功耗、重量和体积（SWaP）受限的应用（如行星探测）中面临挑战。为此，本文提出通过模仿动物的关联学习能力来增强智能机器人的自主性。关联学习使动物能通过记忆同时发生的事件来适应环境。通过复现该机制，类脑机器人可在动态环境中自主导航，并从交互中持续学习以优化性能。本研究探索在开放场迷宫环境中，利用啮齿类动物的空间细胞模型（如位置细胞和网格细胞）实现类脑机器人对关联学习的模拟。通过整合这些生物启发模型，旨在实现空间任务的在线关联学习，推动生物空间认知与机器人技术的融合，为自主系统发展提供新路径。
原文摘要 · Abstract (English)
Data-driven Artificial Intelligence (AI) approaches have exhibited remarkable prowess across various cognitive tasks using extensive training data. However, the reliance on large datasets and neural networks presents challenges such as highpower consumption and limited adaptability, particularly in SWaP-constrained applications like planetary exploration. To address these issues, we propose enhancing the autonomous capabilities of intelligent robots by emulating the associative learning observed in animals. Associative learning enables animals to adapt to their environment by memorizing concurrent events. By replicating this mechanism, neuromorphic robots can navigate dynamic environments autonomously, learning from interactions to optimize performance. This paper explores the emulation of associative learning in rodents using neuromorphic robots within open-field maze environments, leveraging insights from spatial cells such as place and grid cells. By integrating these models, we aim to enable online associative learning for spatial tasks in real-time scenarios, bridging the gap between biological spatial cognition and robotics for advancements in autonomous systems.
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