Modality Selection and Skill Segmentation via Cross-Modality Attention

Fuente: arXiv
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Auteurs principaux: Jiang, Jiawei, Ota, Kei, Jha, Devesh K., Kanezaki, Asako
Format: Preprint
Publié: 2025
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author Jiang, Jiawei
Ota, Kei
Jha, Devesh K.
Kanezaki, Asako
author_facet Jiang, Jiawei
Ota, Kei
Jha, Devesh K.
Kanezaki, Asako
contents Incorporating additional sensory modalities such as tactile and audio into foundational robotic models poses significant challenges due to the curse of dimensionality. This work addresses this issue through modality selection. We propose a cross-modality attention (CMA) mechanism to identify and selectively utilize the modalities that are most informative for action generation at each timestep. Furthermore, we extend the application of CMA to segment primitive skills from expert demonstrations and leverage this segmentation to train a hierarchical policy capable of solving long-horizon, contact-rich manipulation tasks.
format Preprint
id arxiv_https___arxiv_org_abs_2504_14573
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Modality Selection and Skill Segmentation via Cross-Modality Attention
Jiang, Jiawei
Ota, Kei
Jha, Devesh K.
Kanezaki, Asako
Robotics
Artificial Intelligence
Incorporating additional sensory modalities such as tactile and audio into foundational robotic models poses significant challenges due to the curse of dimensionality. This work addresses this issue through modality selection. We propose a cross-modality attention (CMA) mechanism to identify and selectively utilize the modalities that are most informative for action generation at each timestep. Furthermore, we extend the application of CMA to segment primitive skills from expert demonstrations and leverage this segmentation to train a hierarchical policy capable of solving long-horizon, contact-rich manipulation tasks.
title Modality Selection and Skill Segmentation via Cross-Modality Attention
topic Robotics
Artificial Intelligence
url https://arxiv.org/abs/2504.14573