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

arXiv:2607.26047 · cs.RO · Submitted 2026-07-28 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information".

Dev: Acoustic information provides rich cues about object location, material properties, and changes caused by contact or motion.

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So, Dev, I'm really interested in what this paper introduces with S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information. It seems like the core idea is that robots can use sound cues to figure out where to grab things and what they are made of, which is a big step beyond just relying on sight.

Dev: I agree, Rosa, the paper sets up these new acoustic-aware manipulation tasks that force the robot to actually use sound source localization and identification for exploring its surroundings actively. It’s not just passive listening anymore; it’s about using auditory input to determine targets in a physical workspace.

Taro: I think what interests me most is how this handles the situation when things go wrong or when the environment changes unexpectedly; the authors are focused on making sure these acoustic cues help with active exploration, which is crucial when visual information might be missing.

Rosa: Exactly, and they propose a multimodal imitation learning framework called S2A2 that brings together visual features with both acoustic spatial and signal information to handle these complex tasks. It's a way to get richer data for the policy than just what the camera sees.

Dev: From an engineering standpoint, I'm looking at how they structure this, specifically how they integrate different policy architectures like ACT and Diffusion Policy into that framework. We have to consider the latency and make sure the loop rate is fast enough for real-world interaction.

Taro: And I wonder about the robustness of these policies when dealing with unpredictable acoustic environments, especially since we're moving toward tasks where sound dictates action, not just visual input.

Rosa: Well, they found that in simulation experiments, S2A2 was the most effective approach for tasks that required both knowing where to grab something and understanding its timbre. It sounds like the combination of spatial and spectral audio is key there.

Dev: That makes sense because the acoustic spatial map pipeline uses techniques like MUSIC to estimate direction-of-arrival scores, which then gets projected onto a two hundred twenty-four by two hundred twenty-four plane corresponding to the workspace; that's a pretty complex setup we need to monitor closely for stability.

Taro: When we think about real-world application, how long do you anticipate these acoustic-aware systems can operate reliably outside of the controlled simulation environment before performance starts degrading?

Title and authors: Rosa: The paper confirms that real-robot experiments have shown that these proposed tasks and the framework are applicable to actual manipulation, which is encouraging for deployment. However, we still need to see if they maintain that high level of accuracy over long periods in dynamic settings.

Dev: Speaking of deployment, I'm concerned about the processing pipeline; they use a ResNet-eighteen spatial encoder on top of the acoustic spatial map, so we have a significant computational load to manage in real-time.

Taro: If the robot encounters an unexpected sound pattern that doesn't match what it learned from training, how does this system handle that misbehavior? Does it just fail, or does it have a mechanism for recovery?

Rosa: The paper suggests that the multimodal nature of S2A2 gives the policy enough context to make decisions even when the acoustic environment is slightly different than expected, which should help in navigating those unexpected situations.

Dev: That contextual awareness helps with latency management, I guess; if the system can quickly infer a source direction from noisy data, we reduce the time spent waiting for confirmation before executing an action.

Taro: So, if we look at the specific tasks they defined—like the Localization task where you have two visually identical objects and only one makes a sound—does this method handle those visual ambiguities well?

Rosa: Yes, that's exactly what it tackles; for instance, in the Localization task, when selecting which of two visually indistinguishable objects to grasp based on sound source position, the system uses acoustic spatial information to make that choice.

Dev: And then there's the Identification task where you have one object making one of two sounds and multiple boxes; here we need sound identification to select the correct destination box, not just localization of a single source.

Taro: That L andI Task seems particularly interesting because it requires both source localization for target selection and sound identification for destination selection simultaneously, which is a really complex coupling of decisions.

Rosa: And the Exploratory task adds another layer by requiring the robot to actively shake objects to check for sound when neither object makes noise at rest, which is a proactive way to discover things.

Dev: From an engineering loop perspective, I'm thinking about the processing pipeline for acoustic signals; they use STFT with an FFT length of five hundred twelve and fifty percent overlap, which sets up a certain processing requirement we need to keep in mind for low latency execution.

Title and authors: Taro: If we consider the broader impact, how does this shift from vision-centric learning to acoustic-aware learning affect future autonomous systems that operate in complex physical settings?

Rosa: It opens up manipulation possibilities where visual data is insufficient, moving us toward robots that can interact with their environment using a richer sensory input than just sight.

Dev: The implication for latency is that if we can fuse these modalities effectively, we might allow for more complex, multi-step planning sequences without needing a massive amount of pre-programmed state knowledge.

Taro: I see the potential for true autonomous discovery in unstructured settings where visual cues are sparse or misleading, allowing robots to find and interact with hidden elements through sound alone.

Rosa: So we've covered the core mechanics and the specific tasks they set up; before we wrap things up, Dev, what's your final thought on the practical deployment hurdles for this S2A2 framework?

Dev: My main concern remains the computational demands of running four different policy architectures simultaneously while maintaining a fast enough loop rate for dynamic manipulation.

Taro: I just think the way they frame these acoustic-aware tasks shows a path toward agents that aren't just reacting to visual input but are actively seeking information through sound.

Rosa: We've seen how S2A2 moves beyond simple vision by incorporating spatial and signal audio information to solve problems like target selection based on timbre or destination selection based on sound type.

Dev: And while the simulation results are strong for position and timbre tasks, we still need more data to fully quantify the failure modes in high-noise, real-world scenarios that aren't perfectly represented in training.

Taro: Overall, this paper shows how integrating acoustic spatial information into imitation learning can create systems capable of performing active exploration and complex decision-making based on auditory feedback.

Rosa: It's certainly a significant contribution to how we teach robots to interact with the physical world by giving them that extra layer of sensory understanding through sound.

Dev: We'll keep an eye on the latency metrics as we look at integrating these models into our control loops for real-time operation.

Taro: I think this work lays important groundwork for future systems that need to navigate and manipulate objects where visual data is inherently limited or unreliable.

The paper's summary: Rosa: So, to get us back on track, we’re looking at S2A2, which basically tackles how robots can use sound cues—both where they come from and what the sound is—to figure out how to grab things and complete manipulation tasks when vision alone isn't enough.

Dev: That's right; it takes those visual inputs and fuses them with acoustic spatial data, like the direction of arrival, so the robot can actually select a target or a destination based on auditory information.

Taro: I find that idea of active exploration guided by sound really compelling because it means the robot isn't just following a pre-drawn path; it’s using its ears to discover things in an unknown space.

Rosa: Exactly, and the framework uses four different types of policies—like ACT and Diffusion Policy—to handle different needs, which suggests it’s quite flexible for various manipulation challenges.

Dev: From an engineering standpoint, that policy integration is interesting because we need to make sure the overall loop rate stays high enough so that these complex acoustic processing pipelines don't introduce too much delay in action.

Taro: And when the world misbehaves, like an unexpected sound pattern or a visually ambiguous object, how does this framework manage those uncertainties?

Rosa: The paper suggests that by integrating both spatial and signal audio information, the policy gains enough context to make decisions even when the visual input is tricky or incomplete.

Dev: That contextual awareness is vital for reducing uncertainty in control; if the system can quickly infer a source direction from noisy data, we cut down on decision latency significantly.

Taro: It opens up a whole new category of autonomous discovery, where robots can proactively search for hidden objects by listening to subtle sounds rather than just scanning their surroundings.

Rosa: The potential impact here is that we could see manipulation capabilities extended to environments where visual sensors are poor or unreliable, which is a huge step for real-world deployment.

Dev: But Rosa, I still have my concerns about the long-term stability; how long can we expect these acoustic-aware systems to operate reliably outside of a perfectly controlled lab setting?

Taro: We need to see if they can generalize that success to truly unstructured physical environments where the acoustic signatures might change drastically due to noise or material variations.

Rosa: That’s exactly what the real-robot experiments are trying to confirm, and we’re encouraged because they've shown applicability in real-world manipulation scenarios.

Dev: So, if we look at the specific tasks like the Localization and Identification tasks mentioned, what are the core components that make S2A2 succeed where vision alone fails?

Taro: For example, in the L andI Task, it’s about simultaneously using spatial audio to pick a target and sound identification to choose a destination box, which is a really coupled decision-making problem.

Rosa: That coupling of source localization and sound identification is what makes these tasks so demanding for purely visual systems; they require understanding both *where* something is and *what* it sounds like.

Dev: I'm focused on the computational load here; processing that acoustic spatial map, projecting it, and feeding it into a ResNet-eighteen encoder all at once puts a serious strain on our real-time hardware considerations.

Taro: If the system encounters an unexpected sound pattern that doesn't match its training data, how does this framework handle those failures?

Rosa: The authors imply that because it uses multimodal inputs, the policy has enough context to attempt a reasonable action even when things don't look exactly like what it saw during training.

Dev: That contextual safety net is important, but we still need more data to quantify the failure modes in high-noise, real-world scenarios that aren't perfectly represented in the training set.

Taro: So it seems this work points toward a future where manipulation doesn't just rely on sight but can leverage auditory feedback for much deeper environmental understanding.

The paper's improvements: Rosa: So, to recap, the paper lays out how S2A2 moves past just using what a robot sees by incorporating sound information to help it decide what to grab and where to put things, even when things look identical or when the environment is complex.

Dev: That’s right; the core idea is augmenting visual data with acoustic spatial and signal features so the AI has a richer understanding of its physical situation during manipulation.

Taro: The proposed improvements focus on several key areas, like enabling real-time target selection based on sound, inferring object properties through material sounds, and allowing for active exploration guided by auditory cues.

Rosa: It’s really about making the robot smarter in ambiguous situations; it can distinguish objects based on timbre or even determine if something is liquid just by listening to the contact sound.

Dev: That ability to infer material state through acoustics is a big deal for tasks involving liquids or granular materials, and I have to think about how reliable those acoustic inferences are under real-world noise interference.

Taro: And the way they integrate different policy architectures like ACT and Diffusion Policy means the system can be optimized for very different types of complex goals, like long-horizon tasks versus immediate grasping decisions.

Rosa: Exactly, it’s not a one-size-fits-all approach; by mixing those policies, S2A2 can tackle scenarios that require both precise positioning and nuanced auditory interpretation at the same time.

Dev: From an engineering view, that multimodal policy optimization is powerful because it allows us to tailor the model’s behavior for different phases of a manipulation task, which should help manage the required loop rate more effectively.

Taro: The exploration capability is also significant; if a robot can use sound to actively check for hidden objects by shaking things, it fundamentally changes how we think about autonomous discovery in unstructured settings.

Rosa: It means future robots won't just be programmed with fixed paths; they'll be able to use their senses—specifically sound—to probe and interact with the world dynamically.

Dev: I still have my concerns about the computational overhead of running those multiple policy architectures simultaneously while trying to keep the latency low enough for smooth, real-time control.

Taro: That’s a fair point, Dev; we need to ensure that this increased sensory input doesn't translate into an unmanageable processing bottleneck in deployment.

Rosa: The paper does acknowledge that the success in simulation is strong for tasks requiring both position and timbre, but we still need to see sustained performance when those acoustic signatures are distorted by real-world noise.

Dev: So, while the framework shows promise for complex decisions, we’re waiting on more rigorous testing in noisy environments before we can trust it for mission-critical applications.

Taro: The implication is that this research sets a new direction for autonomous agents where sensory fusion moves beyond just visual data and starts incorporating rich physical interaction signals like sound.

Conclusion: Rosa: So, to wrap things up on "S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information," we’ve seen how this framework allows robots to leverage acoustic cues for target selection and identification beyond what vision can provide.

Dev: That’s right; the main contribution is integrating spatial audio maps with visual data through a multimodal policy to handle complex manipulation decisions.

Taro: I think the impact here is that we are moving toward agents that can operate effectively in environments where visual information is inherently limited or ambiguous, opening up a lot of new possibilities for autonomous interaction.

Rosa: It’s exciting because this technology could enable robots to perform more nuanced tasks in industrial settings or even complex domestic scenarios where precise tactile feedback isn't always available.

Dev: I still have my concerns about the deployment duration; how long can we expect these acoustic-aware systems to maintain that level of accuracy when they are actually out in the field for extended periods?

Taro: We need more validation on their robustness under real environmental noise and material variability before we can confidently say this is ready for widespread autonomy.

Rosa: The authors do mention that the real-robot experiments have confirmed applicability, which is encouraging, but sustained long-term reliability remains the biggest question mark for field deployment.

Dev: I agree; until we see consistent performance over a very long duration in unpredictable settings, we have to treat this as a promising research tool rather than a ready-to-deploy system.

Taro: For me, the most exciting part is how this concept of using sound for active exploration could eventually lead to truly adaptive agents that learn and discover their surroundings dynamically.

Rosa: That sounds like a future where robots are less pre-programmed and more capable of intelligent interaction based on what they hear and feel.

Dev: We’ll definitely be watching those latency metrics as we try to fit these sophisticated acoustic processing pipelines into our control loops for actual hardware implementation.

Taro: Speaking of future work, I wonder if this framework could eventually be extended to handle even more complex sensory inputs beyond just spatial audio and visual data.

Rosa: That’s a good thought; extending the multimodal policy to include tactile or haptic feedback could give these robots an even richer understanding of their objects.

Dev: If they can manage that integration without blowing up the loop rate, it could really push the boundaries of what we can achieve in physical manipulation.

Taro: Overall, S2A2 shows a clear path forward for agents that need to understand their physical world through a combination of sight and sound.

Kyoto University · RIKEN, Institute of Science Tokyo

cs.RO

Submitted: 2026-07-28

Updated: 2026-09-29

Comments: Project page: https://azuma413.github.io/projects/s2a2

Code: https://github.com/Genesis-Embodied-AI/Genesis

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 71/100

The gist: Acoustic information provides rich cues about object location, material properties, and changes caused by contact or motion.

Key concepts

Acoustic Spatial Information
This refers to sound cues that provide rich information about object location, material properties, and changes caused by contact or motion. It allows robots to determine where things are based on sound source localization.
S2A2 Framework
This is a multimodal imitation learning framework that combines visual features with acoustic spatial and signal information. It helps the robot gain a richer understanding of its physical situation than vision alone provides for manipulation tasks.
Policy Architectures (ACT and Diffusion Policy)
The framework integrates different policy architectures, such as ACT and Diffusion Policy, to handle various needs. This allows the system to be optimized for different types of complex goals, like precise positioning versus immediate grasping decisions.
Active Exploration
This capability means robots can use sound to proactively discover hidden objects in unknown spaces by shaking things or listening for subtle sounds, rather than just following pre-drawn paths based on visual input.

Terminology

Summary

Acoustic information provides rich cues about object location, material properties, and changes caused by contact or motion. This paper introduces a new set of acoustic-aware manipulation tasks for imitation learning, in which robots must use auditory cues to determine manipulation targets. These tasks require sound source localization and identification for active exploration in robotic manipulation. Also, we propose a multimodal imitation learning framework, Spatial-Spectral Audio Action (S2A2), that integrates visual features with acoustic spatial and acoustic signal information for the acoustic-aware manipulation tasks. We implemented S2A2 models that integrates policies such as ACT, Diffusion Policy, VQ-BeT, and π0, into our framework. Simulation experiments showed that the proposed method is the most effective for tasks requiring both position and timbre. Furthermore, real-robot experiments confirm the applicability of the proposed tasks and framework to real-world manipulation.

The S2A2 framework consists of an environment setup, the S2A2 model, and the acoustic-aware manipulation tasks. The S2A2 model consists of processing pipelines that extract per-modality features from observations and a multimodal policy that integrates them to output the next action.

The problem formulation is: "We formulate this as an imitation learning problem conditioned on visual and auditory observations. Each observation ot consists of an RGB image It, multi-channel acoustic information At, and proprioception st; from At we obtain an acoustic spatial map St and a spectrogram Gt. Based on these observations, the S2A2 model learns a policy πθ that predicts an action sequence at:t+H−1 up to H steps ahead. Training minimizes the imitation learning loss measuring the deviation between predicted and expert actions over a dataset D of expert trajectories: min θ E(ot,at:t+H−1)∼D [LBC (πθ(ot), at:t+H−1)]. (1)"

The acoustic-aware manipulation tasks are defined as follows:

(Table 1 summarizes them)

The Localization task uses two visually indistinguishable objects, only one of which continuously emits sound; the manipulator grasps the sounding object and places it in the box. A single sound is used across all episodes, so the information needed to select the grasp target is the spatial position of the source.

The Identification task uses one object and two boxes. The object continuously emits one of two sounds, and the manipulator transports it to the box corresponding to that sound. The box position for each sound is fixed across episodes. Since there is a single sounding object, the grasp target is uniquely determined from vision and acoustic spatial information is unnecessary; sound identification is required to select the correct destination.

The Localization & Identification task (L&I Task) uses two visually indistinguishable objects and two boxes. Only one object continuously emits sound, which is one of two sounds. The manipulator identifies the grasp target from acoustic spatial information and transports it to the corresponding box based on the sound. The box position for each sound is fixed across episodes. Thus, this task requires source localization for target selection and sound identification for destination selection.

The Exploratory task uses two visually indistinguishable objects and one box. Neither object emits sound at rest, and only one emits sound when moved. The manipulator must grasp and shake each object in turn to check for sound and place the sounding object in the box.

The S2A2 model has three pipelines that process acoustic spatial information, acoustic signal information, and visual information:

(Table 1 summarizes them)

The Acoustic Spatial Map Pipeline estimates the likelihood of source direction from the multi-channel acoustic information recorded by the arrays and projects it onto a 2D map corresponding to the workspace. Specifically, the multiple signal classification (MUSIC) method [26] is applied to the multi-channel signal of each array. Input audio is acquired at a 16 kHz sampling rate and transformed to the frequency domain by a short-time Fourier transform (STFT) with an FFT length of 512 and 50% overlap. The log-normalized spatial spectrum per direction is used as the direction-of-arrival (DOA) score. With an attenuation term based on distance from the array center, it is projected onto a 224 × 224 2D plane corresponding to the array plane of the workspace, yielding the acoustic spatial map S ∈ R 224×224×N, where N is the number of arrays. The acoustic spatial map S is fed to a spatial encoder to produce features usable by the policy network. The spatial encoder is a ResNet-18 with the input channel count extended to N, trained from scratch, and outputs a feature map.

The Spectrogram Pipeline extracts the acoustic signal information of the target source. From the acoustic spatial map S, the source presence likelihood is computed, and peak extraction selects source candidates. Since only one source is present at a time, the highest peak is taken as the candidate.

Improvements for AI systems

Here are specific improvements to AI systems based on the S2A2 framework described in this paper, detailing what these improved systems can achieve:

) Improvements for Robotic Manipulation Systems

The core improvement is the transition from purely vision-based imitation learning to a robust, multimodal system capable of inferring object state and destination from acoustic cues.

  1. 】Real-Time Acoustic Target Selection and Goal Determination

Based on the S2A2 framework, an improved robotic system can perform manipulation tasks where visual ambiguity exists (e.g., selecting one object among visually identical ones).

  1. 】Object State Inference via Material/Contact Sound

The system can infer the material property or contact state of an object through acoustic feedback (e.g., distinguishing between a solid object and a fluid, or detecting the state of a container) by analyzing timbre and contact sounds, which is crucial for tasks like liquid pouring or granular material handling.

  1. 】Active Exploration Guided by Sound Cues

The system can execute active exploration in unknown environments (e.g., searching for a hidden object) by using auditory cues (like tapping or shaking) to detect the presence of a target, allowing the robot to navigate and interact with objects that are not visually obvious.

  1. 】Robust Manipulation Under Occlusion and Variation

The system will maintain high performance in contact-rich manipulation tasks even when visual information is partially occluded or when lighting conditions change, as it relies on acoustic spatial information (where a sound comes from) and signal information (what the sound is) alongside vision.

  1. 】Multimodal Policy Optimization for Complex Goals

By integrating four distinct policy architectures (ACT, Diffusion Policy, VQ-BeT, π0), the system can be optimized to handle different task requirements: ACT excels in tasks requiring both position and timbre; Diffusion Policy shows high success rates on long-horizon tasks like the L&I task; and VQ-BeT/π0 offer strong performance when acoustic information is integrated appropriately.

) Specific System Capabilities Enabled by S2A2

The improved AI system, utilizing the S2A2 framework, can execute the following specific capabilities:

  1. 】Target Selection in Ambiguous Scenes (Localization Task): The robot can successfully grasp one of two visually identical objects based solely on the spatial location of a sound source, even if they are indistinguishable to vision alone.

2.】Destination Selection via Sound Identification (Identification Task): When multiple destinations are present, the robot can correctly choose the target box based on the unique acoustic signature (sound type) emitted by a specific object.

3.】Complex Decision Making in Dynamic Environments (L&I Task): The system can simultaneously perform source localization to select a grasp target and sound identification to determine its correct destination, solving highly coupled decision-making problems that are intractable for vision-only models.

4.】Autonomous Environmental Discovery (Exploratory Task): The robot can proactively search for hidden objects or detect subtle material changes by systematically shaking or tapping objects and interpreting the resulting acoustic feedback, enabling discovery in unstructured settings.

5.】High-Fidelity Sim-to-Real Transfer: The integration of generative audio augmentation (as seen in related works like The Sound of Simulation) allows the learned policies to generalize more effectively from simulation to real robots by training on synthetically diverse acoustic data.

Abstract

Acoustic information provides rich cues about object location, material properties, and changes caused by contact or motion. This paper introduces a new set of acoustic-aware manipulation tasks for imitation learning, in which robots must use auditory cues to determine manipulation targets. These tasks require sound source localization and identification for active exploration in robotic manipulation. Also, we propose a multimodal imitation learning framework, Spatial-Spectral Audio Action (S2A2), that integrates visual features with acoustic spatial and acoustic signal information for the acoustic-aware manipulation tasks. We implemented S2A2 models that integrates policies such as ACT, Diffusion Policy, VQ-BeT, and π 0, into our framework. Simulation experiments showed that the proposed method is the most effective for tasks requiring both position and timbre. Furthermore, real-robot experiments confirm the applicability of the proposed tasks and framework to real-world manipulation.

Sources

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