LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music

• URL: http://arxiv.org/abs/2606.31158v1
• Date: Tue, 30 Jun 2026 05:35:26 GMT
• Title: LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music
• Abstract summary: This paper introduces a novel framework that synthesizes complex robotic actions from a rich tapestry of multimodal human inputs.<n>Our system architecture integrates a speech transcription model, a gesture recognition module, and a signal processing pipeline for beat detection.<n>The framework has ability to interpret and fuse semantic commands from speech, deictic information from gestures, and rhythmic cues from music.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quest for intuitive and natural human-robot interaction (HRI) remains a significant challenge in robotics. Traditional methods often rely on rigid, pre-programmed commands that limit the robot's expressiveness and adaptability. This paper introduces a novel framework that leverages the reasoning capabilities of Large Language Models (LLMs) to synthesize complex robotic actions from a rich tapestry of multimodal human inputs: natural speech, hand gestures, and music/sound beats. Our system architecture integrates a speech transcription model, a gesture recognition module, and a signal processing pipeline for beat detection. These processed inputs are contextualized using prompt templates and fed into a LLM. The LLM, informed by a predefined robot action space, reasons over the combined inputs to generate a coherent sequence of actions. This sequence is dispatched to an action queue for execution on a quadruped robot over ROS. The framework has ability to interpret and fuse semantic commands from speech, deictic information from gestures, and rhythmic cues from music. This work represents a step towards creating robots that can interact with humans in a more fluid, creative, and context-aware manner.

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