Integrating ImplicitRDP with auditory signals improves performance in dynamic, noisy environments for contact-rich manipulation tasks.

Motivation

The current ImplicitRDP approach effectively integrates visual and force data, but real-world environments often include significant auditory information that can influence manipulation tasks, particularly in noisy or dynamic settings. By incorporating auditory signals, the model could potentially be more robust and adaptable to these conditions.

Proposed Method

Extend the ImplicitRDP framework to include an auditory modality by training a modified model on datasets that include synchronized audio, video, and force data. Evaluate the model's performance in environments with varying levels of noise and dynamic changes, comparing it against the current visual-force model.

Expected Contribution

This research would broaden the applicability of the ImplicitRDP framework, demonstrating its potential in more complex and realistic environments, thus providing insights into multi-modal integration in robotics.

Required Resources

Compute resources for training the extended model, datasets containing audio, video, and force data, expertise in audio signal processing and robotics.

Source Paper

ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

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