Quartz 5

Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper

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descriptionHandheld grippers are increasingly used to collect human demonstrations due to their ease of deployment and versatility. However, most existing designs lack tactile sensing, despite the critical role of tactile feedback in precise manipulation. We present a portable, lightweight gripper with integrated tactile sensors that enables synchronized collection of visual and tactile data in diverse, real-world, and in-the-wild settings. Building on this hardware, we propose a cross-modal representation learning framework that integrates visual and tactile signals while preserving their distinct characteristics. The learned representations are interpretable and consistently emphasize contact regions during physical interactions. When used for downstream manipulation tasks, these representations enable more efficient and effective policy learning, supporting precise robotic manipulation based on multimodal feedback. We validate our approach on fine-grained tasks such as test tube insertion and pipette-based fluid transfer, demonstrating improved accuracy and robustness under external disturbances.
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1 min read

3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing

Binghao Huang1,
Yixuan Wang1,
Xinyi Yang2,
Yiyue Luo3,
Yunzhu Li1

Conference on Robot Learning ( CoRL), 2024

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