Learning Robot Manipulation from Audio World Models
Fan Zhang, Michael Gienger
2025-12-16
Summary
This paper focuses on improving how robots learn to perform tasks by helping them understand and predict sounds, not just see what's happening.
What's the problem?
Many real-world tasks, like pouring a drink or interacting with objects while music is playing, require a robot to understand both what it sees *and* what it hears. Relying on vision alone isn't enough because sounds provide important clues about what's going on and what will happen next, but current robots struggle to use sound effectively to plan ahead.
What's the solution?
The researchers developed a new system that lets a robot 'imagine' future sounds. It uses a technique called generative latent flow matching to predict how audio will change over time. This allows the robot to anticipate the consequences of its actions based on what it expects to hear, making its movements more accurate and efficient.
Why it matters?
This work is important because it shows that robots need to be able to predict future sounds to successfully complete complex tasks in the real world. It's not enough to just process what a robot is currently seeing and hearing; it needs to understand the underlying patterns in sound, like rhythm, to make smart decisions and learn effectively.
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.