Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents
This work addresses improving communication comfort in human-agent interactions, but it is incremental as it builds on existing cognitive agent and proxemics research with a new environment.
The paper tackled the problem of enabling agents to learn proxemic behavior for human-agent interaction by proposing a modified gridworld environment where an issuer provides disagreement signals, and found that the learning agent can identify the proxemic space when given feedback.
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.