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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.20444 |
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| _version_ | 1866915949074972672 |
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| author | Hua, Qianxi Li, Xinyue Yan, Zheng Li, Yang Zhang, Chi Li, Yongyao Liu, Yufei |
| author_facet | Hua, Qianxi Li, Xinyue Yan, Zheng Li, Yang Zhang, Chi Li, Yongyao Liu, Yufei |
| contents | Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging. This is largely due to the lack of datasets with rich physical interaction signals, systematic task organization, and sufficient scale. To address these limitations, we introduce the VTOUCH dataset. It leverages vision based tactile sensing to provide high-fidelity physical interaction signals, adopts a matrix-style task design to enable systematic learning, and employs automated data collection pipelines covering real-world, demand-driven scenarios to ensure scalability. To further validate the effectiveness of the dataset, we conduct extensive quantitative experiments on cross-modal retrieval as well as real-robot evaluation. Finally, we demonstrate real-world performance through generalizable inference across multiple robots, policies, and tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20444 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation Hua, Qianxi Li, Xinyue Yan, Zheng Li, Yang Zhang, Chi Li, Yongyao Liu, Yufei Robotics Artificial Intelligence Databases Machine Learning Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging. This is largely due to the lack of datasets with rich physical interaction signals, systematic task organization, and sufficient scale. To address these limitations, we introduce the VTOUCH dataset. It leverages vision based tactile sensing to provide high-fidelity physical interaction signals, adopts a matrix-style task design to enable systematic learning, and employs automated data collection pipelines covering real-world, demand-driven scenarios to ensure scalability. To further validate the effectiveness of the dataset, we conduct extensive quantitative experiments on cross-modal retrieval as well as real-robot evaluation. Finally, we demonstrate real-world performance through generalizable inference across multiple robots, policies, and tasks. |
| title | VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation |
| topic | Robotics Artificial Intelligence Databases Machine Learning |
| url | https://arxiv.org/abs/2604.20444 |