Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

TL;DR

This paper models human-like motivation in mobile robots using a computational approach based on Hull's theory, incorporating needs, preferences, and reinforcement learning to improve decision-making and adaptive behavior.

Contribution

It introduces a motivational-based learning model for robots that integrates needs and preferences, with reinforcement learning, inspired by human motivation theories.

Findings

Agents learned better strategies aligned with their metabolism.

Pleasure significantly influenced behavior, especially in slow metabolism agents.

Agents prioritized survival over pleasure in harsh environments.

Abstract

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.