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

The study proposes a multimodal framework that uses large language models and vision-language models to translate human purposes expressed in natural language into robot missions and drives, aligning the behavior of open-ended learning autonomous robots with human intent. The system has three layers—semantic alignment, mission generation, and drive modeling—forming a bidirectional interaction loop in which human input defines high-level purposes, the robot expands them into executable missions, and feedback maintains alignment. Simulation and physical robot experiments show that the pipeline can generate effective missions and behaviors.

Part of the event: Multimodal Framework Aligns Human Language Goals as Robot Drives

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