LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music
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 templat...
| Authors | Snehasis Banerjee, Ranjan Dasgupta |
|---|---|
| Primary category | cs.RO |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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