Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents

TL;DR

This paper explores how reinforcement learning agents can learn proxemic behavior in a modified gridworld environment, enabling more natural human-agent interactions by adapting to personal space preferences through feedback.

Contribution

It introduces a new gridworld environment where agents learn proxemic behavior via feedback, addressing the limitation of fixed personal space assumptions in prior work.

Findings

Agents successfully identify proxemic space with feedback

Learning improves interaction comfort and adaptability

Proposed environment facilitates proxemic behavior learning

Abstract

Proxemics is a branch of non-verbal communication concerned with studying the spatial behavior of people and animals. This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being. With increasing research on human-agent interaction, new alternatives are needed that allow optimal communication, avoiding agents feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, environments consider fixed personal space and that the agent previously knows it. In this work, we aim to study how agents behave in environments based on proxemic behavior, and propose a modified gridworld to that aim. This environment considers an issuer with proxemic behavior that provides a disagreement signal to the agent. Our results show that the learning agent can identify the proxemic space when the issuer gives feedback about agent performance.