Ambient Adventures: Teaching ChatGPT on Developing Complex Stories

TL;DR

This paper explores using large language models to generate and simplify stories for guiding agents in imaginary play within a simulated environment, aiming to enhance robot engagement and creativity.

Contribution

It introduces a method for generating and simplifying stories from LLMs to enable agents to participate in complex imaginary play scenarios.

Findings

Generated stories effectively guide agent actions in the simulation.

Simplification of stories improves agent understanding and performance.

The approach demonstrates potential for enhancing robot creativity and interaction.

Abstract

Imaginative play is an area of creativity that could allow robots to engage with the world around them in a much more personified way. Imaginary play can be seen as taking real objects and locations and using them as imaginary objects and locations in virtual scenarios. We adopted the story generation capability of large language models (LLMs) to obtain the stories used for imaginary play with human-written prompts. Those generated stories will be simplified and mapped into action sequences that can guide the agent in imaginary play. To evaluate whether the agent can successfully finish the imaginary play, we also designed a text adventure game to simulate a house as the playground for the agent to interact.