用大脑神经机制启发机器人自主发现新目标，提升学习灵活性。
Dynamic Neural Curiosity Enhances Learning Flexibility for Autonomous Goal Discovery
- 结合注意力与好奇机制，模拟脑内去甲肾上腺素系统
- 在不同难度物体上实现目标探索与学习轨迹多样化
- 适合研究自主学习、强化学习与类脑机器人系统者
机器人自主学习新目标仍是复杂挑战。本文提出一种融合好奇与注意力的模型，借鉴蓝斑-去甲肾上腺素系统及认知持久性、视觉习惯化等机制。通过模拟机械臂在不同难度物体上的实验，机器人先通过运动泛化（motor babbling）和返回抑制机制进行自下而上的注意力引导，再因好奇机制引发的神经活动启动目标学习。该架构采用动态神经场建模，利用多层感知机实现前向与逆向模型，支持推动物体至不同方向等目标学习。动态神经场使机器人在不同物体上表现出多样化的学习路径，并展现出对相似目标的学习能力以及探索与利用之间的持续切换特性。
原文摘要 · Abstract (English)
The autonomous learning of new goals in robotics remains a complex issue to address. Here, we propose a model where curiosity influence learning flexibility. To do so, this paper proposes to root curiosity and attention together by taking inspiration from the Locus Coeruleus-Norepinephrine system along with various cognitive processes such as cognitive persistence and visual habituation. We apply our approach by experimenting with a simulated robotic arm on a set of objects with varying difficulty. The robot first discovers new goals via bottom-up attention through motor babbling with an inhibition of return mechanism, then engage to the learning of goals due to neural activity arising within the curiosity mechanism. The architecture is modelled with dynamic neural fields and the learning of goals such as pushing the objects in diverse directions is supported by the use of forward and inverse models implemented by multi-layer perceptrons. The adoption of dynamic neural fields to model curiosity, habituation and persistence allows the robot to demonstrate various learning trajectories depending on the object. In addition, the approach exhibits interesting properties regarding the learning of similar goals as well as the continuous switch between exploration and exploitation.
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