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 S2A2 framework targets acoustic-aware manipulation tasks in which the manipulation target or destination cannot be uniquely determined from vision alone, and determines actions using acoustic spatial and acoustic signal information in addition to vision.
Problem Formulation
The S2A2 framework targets acoustic-aware manipulation tasks in which the manipulation target or destination cannot be uniquely determined from vision alone, and determines actions using acoustic spatial and acoustic signal information in addition to vision. The specific loss depends on the policy network; details are given in the Policy Architecture and Training section.
Processing Pipeline
The S2A2 model has three pipelines that process acoustic spatial information, acoustic signal information, and visual information. Each pipeline consists of preprocessing and an encoder appropriate to its input modality. Except for the RGB image pipeline, which directly uses the vision encoder of the underlying policy, the acoustic spatial map pipeline and the spectrogram pipeline are described below.
All microphone arrays are placed on the same plane as the workspace. 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 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.
Multimodal Policy
As described in the processing pipeline section, each pipeline outputs a feature map. The format for feeding these feature maps to the policy network differs by policy architecture.
Real-Robot Experiments
Two tasks were selected for evaluation: the L&I task, which requires both localization and identification capabilities, and the exploratory task, which requires active exploration. Their success rates confirm that for tasks difficult to solve with the vision-only baseline, the S2A2 model integrating acoustic information is also effective in the real world.
Conclusion
Acoustic-aware manipulation tasks were introduced, in which the manipulation target or destination cannot be uniquely determined from vision alone, and S2A2 is proposed as an imitation learning framework that uses acoustic information. Simulation experiments showed the importance of using representations matched to the acoustic capabilities a task requires, that unnecessary acoustic modalities can degrade performance, and that the efficiency of using acoustic information depends on the policy architecture. Real-robot experiments further confirmed the effectiveness of S2A2, which outperformed the vision-only baseline.
Limitations
The final camera-ready version will include acknowledgments, including thanks to reviewers who gave useful comments, to colleagues who contributed to the ideas, and to funding agencies and corporate sponsors that provided financial support.
Differences Between Simulation and Real-World Experiments
In the real-robot experiments, the same pipelines as in simulation are used for the acoustic spatial map and the spectrogram. However, to account for the microphone placement, the robot configuration, and the noise characteristics present in real recordings, the following differences were introduced.
Extraction of Latent States
As the intermediate-layer representation, for ACT the output of the transformer encoder that encodes the observation sequence was used. For Diffusion Policy, the features near the bottleneck connecting the U-Net encoder and decoder were used. For VQ-BeT, the output of the middle layer of the transformer blocks contained in the GPT-style policy network was used. For the foundation model, the final hidden state on the foundation-model side that integrates visual and language features was used. Because the same U-Net is applied multiple times during the denoising process in Diffusion Policy, the features obtained at the midpoint of this process were used as the intermediate-layer representation.
The features were recorded at each policy inference. Both the final-layer and intermediate-layer representations were averaged along the sequence axis and treated as a single latent vector per inference step. Each point in the t-SNE plot corresponds to the features of an entire action sequence generated by one inference. For t-SNE, the output dimensionality was set to 2, PCA initialization was used, the learning rate was set automatically, and all random seeds were fixed.
Frequently Asked Questions
How does S2A2 differ from standard imitation learning approaches for robot manipulation? S2A2 integrates acoustic spatial and acoustic signal information in addition to vision, enabling manipulation in tasks where the target or destination cannot be uniquely determined from visual cues alone.
What acoustic processing methods does the S2A2 framework employ? The framework uses the MUSIC method applied to multi-channel audio signals recorded by microphone arrays to estimate source direction likelihood, and projects this information onto a 2D map corresponding to the workspace.
Does adding acoustic information always improve robot manipulation performance? No, simulation experiments showed that unnecessary acoustic modalities can degrade performance, and the efficiency of using acoustic information depends on the specific policy architecture used.
Was the S2A2 framework tested on real robots or only in simulation? Both simulation and real-robot experiments were conducted, with real-robot results confirming the effectiveness of S2A2 over the vision-only baseline for acoustically challenging tasks.