Ambient Adventures: Teaching ChatGPT on Developing Complex Stories
This work addresses the challenge of enhancing creativity and personification in human-robot interaction, though it appears incremental by applying existing LLM capabilities to a new domain.
The paper tackled the problem of enabling robots to engage in imaginative play by using large language models to generate stories from human prompts, which are then simplified into action sequences for guiding agents in a simulated house environment.
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.