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arXiv AI By Diana Cerdas-Vargas, Alejandro Romero, Richard J. Duro

From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Purposes

Read the original on arXiv AI →

The paper presents a multimodal framework that aligns open-ended learning autonomous robots with human purposes using large language models (LLMs) and vision-language models (VLMs). It transforms human intentions expressed in natural language into robot missions and drives through a three-layer architecture—semantic alignment, mission generation, and drive modeling—allowing bidirectional interaction. Experiments in simulation and on a physical robot show that the system generates valid missions and behaviors, maintaining alignment with human intent.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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