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BotDirector simplifies robot storytelling for children by combining tangible interactions with LLM-generated narratives and swarm robot execution. This system bridges the gap between complex robotics and young users, allowing them to create flexible dramas using everyday objects. The approach demonstrates a practical application of multi-modal AI and autonomous navigation in educational and creative contexts.
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BotDirector: Robot Storytelling Across the Symmetrical Reality with Multi-modal Interactions

arXiv2 min read

arXiv:2606.03223v1 Announce Type: cross Abstract: Robot storytelling offers a unique blend of technological innovation and creative expression that engages children in unprecedented ways. However, the technical aspects are often too complicated for children. We propose an interactive system that facilitates robot storytelling with tangible and natural language interactions. Children arrange the playground with their own stuff and create narratives with an LLM agent. The created narratives are transformed into a motion sequence based on the map and characters, and the motions are executed by self-navigating swarm robots. This system enhances robot storytelling with flexible scenarios, enabling young children to create robot dramas with everyday objects.

Abstract:Robot storytelling offers a unique blend of technological innovation and creative expression that engages children in unprecedented ways. However, the technical aspects are often too complicated for children. We propose an interactive system that facilitates robot storytelling with tangible and natural language interactions. Children arrange the playground with their own stuff and create narratives with an LLM agent. The created narratives are transformed into a motion sequence based on the map and characters, and the motions are executed by self-navigating swarm robots. This system enhances robot storytelling with flexible scenarios, enabling young children to create robot dramas with everyday objects.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2606.03223 [cs.RO]

(or arXiv:2606.03223v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.03223 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/VRW70859.2026.00227 Focus to learn more DOI(s) linking to related resources

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Full text under the publisher's open licence · 206 words

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