论文/arXiv 2609.23352·1 个月前

BiView-Touch：通过跨手补全学习双手触觉表征

BiView-Touch用跨手潜变量补全学习双手触觉表征，并在HumanTouch与BVT-20上验证低标注和迁移表现。

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原文链接：BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion（arXiv 2609.23352 · 发布日期：2026-09-20）

BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion

BiView-Touch用跨手潜变量补全学习双手触觉表征，并在HumanTouch与BVT-20上验证低标注和迁移表现。

核心要点

• 双手交互产生互补触觉视图，但既有表征学习较少探索跨手关系。
• 框架从可见目标手区域和同步对侧手，补全被掩码的目标手潜变量。
• 在HumanTouch低标注设置下，冻结表征持续优于代表性自监督基线。
• 作者引入BVT-20双侧触觉数据集，代码和数据集细节见匿名项目页。

验证范围与局限：验证范围为HumanTouch和BVT-20上的识别、跨会话/预训练语料迁移及留出双手任务。

英文摘要 (Abstract)

Bimanual interaction produces complementary tactile views of the same physical process, yet existing tactile representation learning largely models the two hands independently or combines them only for downstream prediction, leaving their cross-hand relationship unexplored. To exploit this overlooked structure, we introduce BiView-Touch, a tactile-only framework that completes masked target-hand latents from the remaining visible target-hand regions and the synchronized full contralateral hand. A student encoder with a geometry-conditioned directional decoder predicts full-view EMA latent targets, while temporal and layout counterfactuals encourage sensitivity to synchronized and anatomically organized source information. Controlled ablations and source-context interventions show that BiView-Touch learns structured cross-hand dependence on temporally aligned and anatomically organized contralateral tactile context, rather than benefiting from bilateral input alone. On the public HumanTouch dataset, its frozen representations consistently outperform representative self-supervised baselines across low-label settings. With only 5% downstream labels, BiView-Touch achieves relative balanced-accuracy gains of 7.1% on bilateral wrist-motion recognition and 14.1% on force-derived interaction-phase recognition. We further introduce BVT-20, a 20-task bilateral tactile dataset, and demonstrate transfer across recording sessions and pretraining corpora, including transfer to a held-out bimanual task. Our code and dataset details are available on the anonymous project page: https://anonymous.4open.science/w/biview-touch-review-site-050C/.

作者：Chenxin Liang, Youchen Lai, Chuqiao Lyu, Tianxing Chen, Shoujie Li, Wenbo Ding

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.23352：BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion访问 2026-10-21