Hamburg Sarah, Jimenez Rodriguez Alejandro, Htet Aung, Di Nuovo Alessandro
Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK.
Entropy (Basel). 2024 Jul 9;26(7):582. doi: 10.3390/e26070582.
Taking inspiration from humans can help catalyse embodied AI solutions for important real-world applications. Current human-inspired tools include neuromorphic systems and the developmental approach to learning. However, this developmental neurorobotics approach is currently lacking important frameworks for human-like computation and learning. We propose that human-like computation is inherently embodied, with its interface to the world being neuromorphic, and its learning processes operating across different timescales. These constraints necessitate a unified framework: active inference, underpinned by the free energy principle (FEP). Herein, we describe theoretical and empirical support for leveraging this framework in embodied neuromorphic agents with autonomous mental development. We additionally outline current implementation approaches (including toolboxes) and challenges, and we provide suggestions for next steps to catalyse this important field.
从人类身上获取灵感有助于催生适用于重要现实世界应用的具身人工智能解决方案。当前受人类启发的工具包括神经形态系统和学习的发展方法。然而，这种发展性神经机器人方法目前缺乏用于类人计算和学习的重要框架。我们提出，类人计算本质上是具身的，其与世界的接口是神经形态的，其学习过程在不同时间尺度上运行。这些限制需要一个统一的框架：以自由能原理（FEP）为基础的主动推理。在此，我们描述了在具有自主心理发展的具身神经形态智能体中利用这一框架的理论和实证支持。我们还概述了当前的实现方法（包括工具箱）和挑战，并为推动这一重要领域的后续步骤提供了建议。
