Learning Robot Manipulation from Audio World Models

2025-12-09

AI Summary

This work addresses the limitation of vision-only perception in robotic manipulation, which hinders robust multimodal reasoning. We propose an audio-centric world model framework for action learning that explicitly models the temporal evolution, physical attributes, and intrinsic rhythmic patterns of audio—going beyond conventional methods that rely solely on instantaneous observations. Methodologically, we introduce a generative latent flow matching model that jointly optimizes multimodal representation learning and temporal prediction, enabling high-fidelity forecasting of future audio states. This predictive capability is integrated into the robot’s policy learning pipeline to support audio-guided, temporally extended action planning. Evaluated on two real-world tasks—audio-driven manipulation and music-synchronized operation—our approach significantly outperforms non-predictive baselines. Results demonstrate that future audio prediction is critical for long-horizon action reasoning and establish the first deep integration of dynamic audio modeling with closed-loop robotic control.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

World models have demonstrated impressive performance on robotic learning tasks. Many such tasks inherently demand multimodal reasoning; for example, filling a bottle with water will lead to visual information alone being ambiguous or incomplete, thereby requiring reasoning over the temporal evolution of audio, accounting for its underlying physical properties and pitch patterns. In this paper, we propose a generative latent flow matching model to anticipate future audio observations, enabling the system to reason about long-term consequences when integrated into a robot policy. We demonstrate the superior capabilities of our system through two manipulation tasks that require perceiving in-the-wild audio or music signals, compared to methods without future lookahead. We further emphasize that successful robot action learning for these tasks relies not merely on multi-modal input, but critically on the accurate prediction of future audio states that embody intrinsic rhythmic patterns.

Problem

• Learning robot manipulation using audio world models
• Anticipating future audio for long-term reasoning
• Enhancing multimodal tasks with audio prediction

Innovation

• Generative latent flow matching model for audio prediction
• Integrating future audio anticipation into robot policy
• Focus on predicting audio states with rhythmic patterns