💻 computer science

S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information

This paper introduces S2A2, a multimodal imitation learning framework that integrates visual features with acoustic spatial and signal information to enable robots to effectively perform manipulation tasks by utilizing auditory cues for target localization and identification.

Original authors: Kaneyoshi Hiratsuka, Benjamin Yen, Ryosuke Kojima

Published 2026-07-29

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CC BY 4.0

Original authors: Kaneyoshi Hiratsuka, Benjamin Yen, Ryosuke Kojima

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine teaching a robot to do chores, like picking up a specific toy or pouring a drink. Usually, we teach robots by showing them videos of humans doing the task, a method called "imitation learning." The robot looks at the video, sees the objects, and learns to copy the movements. But here's the catch: robots are often terrible at seeing what they can't see. If a toy is hidden behind a curtain, or if two identical-looking boxes are sitting on a table, a robot that only uses its eyes gets confused. It doesn't know which box to grab or which one to put the toy in. This is where the idea of "multimodal" learning comes in. Just like humans use their ears to hear a crunch when they step on a twig or their sense of touch to feel if a surface is slippery, robots can be taught to use sound and touch to understand the world better. Scientists have long known that sound carries clues about what an object is made of and where it is, but teaching robots to use these clues to make decisions has been a tricky puzzle.

This paper, titled "S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information," tackles that puzzle by giving robots a pair of "super-ears" to go along with their eyes. The researchers, from Kyoto University and RIKEN, created a new framework called S2A2 (Spatial-Spectral Audio Action). Think of S2A2 as a special translator that helps a robot understand two different types of sound information at once: where a sound is coming from (spatial) and what the sound actually is (spectral/timbre).

In the real world, the team tested this on a robot arm equipped with multiple microphones arranged in a circle around its workspace. They set up four different challenges to see if the robot could learn to use sound. In one challenge, two identical-looking blocks were on the table, but only one was making a noise; the robot had to find the noisy one. In another, the robot had to listen to a single object and decide which of two boxes it belonged to based on the sound it made. The most complex challenge involved shaking two silent-looking cans to see which one rattled, then putting the rattling one in a box.

The results were promising but nuanced. In computer simulations, the S2A2 framework proved to be the most effective way to teach robots these tasks, especially when they needed to figure out both where a sound was and what it was. The robot learned to combine the visual image of the table with a "heat map" of sound locations and a spectrogram (a visual picture of the sound's frequency) to make smart choices. However, the researchers found that you can't just throw all the data at the robot; if you give it sound information it doesn't need for a specific task, it sometimes gets confused and performs worse.

When they moved from the computer simulation to a real robot in a real room, the S2A2 system still worked better than a robot that only used its eyes. The real-world tests confirmed that robots can indeed learn to listen to their environment to solve problems that are impossible to solve by sight alone. The paper suggests that while this approach is a significant step forward, the way sound is integrated depends heavily on the specific "brain" (or policy) the robot is using. Some robot brains learned the sound clues quickly, while others struggled a bit more, suggesting that future work needs to figure out the best way to mix hearing and seeing for different types of robot learners. Ultimately, this research shows that giving robots the ability to listen and localize sound can help them navigate a world where things aren't always perfectly visible.

Technical Summary: S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information

Problem Statement
Current imitation learning frameworks for robot manipulation predominantly rely on visual observations and language instructions. While effective for many tasks, these vision-centric approaches struggle when manipulation targets are visually indistinguishable, occluded, or when critical state information (such as material properties or internal contents) is not visible. Existing research integrating non-visual modalities has largely focused on touch or single-channel contact sounds. However, acoustic information offers distinct advantages: it provides cues about object location, material properties, and dynamic states (e.g., flow rates, liquid levels) that are invisible to cameras. Furthermore, acoustic data contains both signal information (what is happening, e.g., timbre) and spatial information (where it is happening). The paper identifies a gap in leveraging acoustic spatial information—specifically sound source localization via microphone arrays—integrated with signal processing for robotic manipulation policies.

Methodology: The S2A2 Framework
The authors propose Spatial-Spectral Audio Action (S2A2), a multimodal imitation learning framework designed to integrate acoustic spatial and acoustic signal information with visual observations.

Acoustic-Aware Manipulation Tasks:
To evaluate the framework, four specific tasks were defined where vision alone is insufficient:
- Localization: Selecting a sound-emitting object among visually identical silent objects.
- Identification: Transporting a single object to a specific destination box based on its emitted timbre.
- Localization & Identification (L&I): Selecting a sound-emitting object from identical silent ones and transporting it to a destination determined by its specific timbre.
- Exploratory: Actively shaking visually identical objects to elicit sound from one, then grasping and placing the sounding object.
Processing Pipeline:
The S2A2 model processes three distinct modalities:
- Visual: Standard RGB images processed by the underlying policy's vision encoder.
- Acoustic Spatial Map: Derived from multi-channel audio using the MUSIC (Multiple Signal Classification) algorithm. This estimates the likelihood of sound source direction and projects it onto a 2D map corresponding to the workspace.
- Acoustic Spectrogram: Derived using Spotforming (a technique combining multiple microphone arrays with Non-negative Matrix Factorization). This isolates the acoustic signal of the specific source identified in the spatial map, suppressing ambient noise and co-directional interference.
Multimodal Policy:
The framework integrates these features into a policy network to predict action sequences. The authors implemented S2A2 with four distinct policy architectures: ACT (Action Chunking with Transformers), Diffusion Policy, VQ-BeT (Vector Quantized Behavior Transformer), and $\pi_0$ (a Vision-Language-Action flow model). The features from the spatial map and spectrogram are concatenated with proprioception and visual features before being fed into the policy network.

Key Contributions

Task Definition: The introduction of "acoustic-aware manipulation tasks" where target selection, goal determination, or active exploration are governed by a spatially distributed sound environment.
Framework Proposal: The development of S2A2, which uniquely combines an acoustic spatial map (representing where sound is) with a spectrogram (representing what the sound is) within an imitation learning pipeline.
Systematic Evaluation: A comprehensive evaluation in both simulation and real-world environments, analyzing the specific contributions of spatial vs. signal acoustic information across different policy architectures.

Experimental Results

Simulation: Experiments compared the full S2A2 model against baselines using only vision, vision + spatial, and vision + signal.
- The full S2A2 model achieved the highest success rates, particularly in the L&I task (78.7% for ACT, 89.7% for Diffusion Policy), which requires both localization and identification.
- Models lacking the necessary modality (e.g., using only spectrograms for a localization task) performed significantly worse, often failing to outperform the vision-only baseline.
- Latent Space Visualization: t-SNE analysis revealed that the S2A2 model's latent space clearly separated sound types and source coordinates, whereas models missing specific acoustic inputs showed weaker separation, correlating with lower task success.
Real-Robot Experiments: Using an ILOHA bimanual manipulator and four circular microphone arrays, the authors validated the framework in the real world.
- The S2A2 model (specifically with ACT) successfully outperformed the vision-only baseline on the L&I and Exploratory tasks, confirming the applicability of the approach to real-world manipulation with physical noise and lighting variations.

Significance and Claims
The paper claims that acoustic information is a critical, complementary modality for robotic manipulation, particularly when visual cues are ambiguous or insufficient. The authors emphasize that:

Modality Matching is Crucial: The effectiveness of acoustic information depends on matching the representation to the task requirements. Adding unnecessary acoustic modalities (e.g., signal information for a pure localization task) can degrade performance, likely due to the difficulty of filtering irrelevant inputs with limited demonstration data.
Policy Dependency: The efficiency of integrating acoustic information varies significantly across policy architectures. ACT and Diffusion Policy showed marked improvements, while VQ-BeT showed modest gains, and $\pi_0$ showed variable results, suggesting that the design of large-scale pretrained models requires careful consideration when integrating new modalities like spatial audio.
Active Exploration: The framework enables robots to perform active exploration (e.g., shaking objects) to resolve ambiguity, a capability that is difficult to achieve with vision alone.

The authors conclude that while the current work demonstrates effectiveness in controlled environments with limited sound sources, generalization to diverse platforms, complex environments with simultaneous sound sources, and large-scale pretrained models remains a direction for future work.

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