收集人类操作机器人抓握软物体的多模态数据，助力复杂触觉学习。
A Humanoid Visual-Tactile-Action Dataset for Contact-Rich Manipulation
- 用拟人机器人远程操控采集视觉-触觉-动作数据
- 覆盖多种压力条件下的软物操作场景
- 适合研究触觉感知与柔顺控制的学者
接触丰富的操作在机器人学习中日益重要。然而，以往的机器人学习数据集主要关注刚性物体，未能充分反映现实操作中压力条件的多样性。为弥补这一不足，我们提出一个面向柔性软物体操作的拟人化视觉-触觉-动作数据集。该数据集通过配备灵巧手的拟人机器人进行遥操作采集，记录了在不同压力条件下产生的多模态交互信息。本工作也推动未来研究开发具备先进优化策略的模型，以有效利用触觉信号的复杂性和多样性。
原文摘要 · Abstract (English)
Contact-rich manipulation has become increasingly important in robot learning. However, previous studies on robot learning datasets have focused on rigid objects and underrepresented the diversity of pressure conditions for real-world manipulation. To address this gap, we present a humanoid visual-tactile-action dataset designed for manipulating deformable soft objects. The dataset was collected via teleoperation using a humanoid robot equipped with dexterous hands, capturing multi-modal interactions under varying pressure conditions. This work also motivates future research on models with advanced optimization strategies capable of effectively leveraging the complexity and diversity of tactile signals.
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