认知移动机器人中基于动机模型的目标导向运动学习
Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots
AI总结：
本文通过计算建模Hull动机理论并结合强化学习，研究了移动机器人在不同代谢速率与环境中如何通过学习平衡需求与愉悦偏好以完成目标导向运动，发现愉悦机制显著影响行为学习，尤其是慢代谢智能体。
摘要：
人类的需求会根据强度和情境来驱动其行为。然而，我们也会对每个动作所感知到的愉悦感形成偏好，而这种偏好会随时间发生变化。这使得决策变得更加复杂，需要通过学习来根据情境平衡需求与偏好。为了理解这一过程的工作原理，并推动具备基于动机学习模型的机器人开发，我们对Hull提出的动机理论进行了计算建模。在该模型中，智能体（移动机器人的一种抽象）被驱动以维持自身的稳态（homeostasis）。我们加入了享乐维度，以考察偏好如何影响决策，并采用强化学习来训练基于动机的智能体。我们在两种不同环境中运行了三个具有不同能量衰减速率（代表不同代谢水平）的智能体，以观察这些因素对其策略、运动和行为的影響。结果表明，在能够提供更适合其代谢水平选择的环境中，智能体学到了更优的策略。在动机机制中引入愉悦感对行为学习产生了显著影响，尤其是对代谢较慢的智能体而言。当生存受到威胁时，智能体会忽略愉悦感和平衡状态，这提示了在严酷场景下应如何行动。
英文摘要：
Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action's perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
