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.
翻译：接触丰富的操作在机器人学习中日益重要。然而，先前关于机器人学习数据集的研究多集中于刚性物体，未能充分体现现实世界操作中压力条件的多样性。为填补这一空白，我们提出了一个专为操作可变形软体物体设计的人形机器人视觉-触觉-动作数据集。该数据集通过配备灵巧手的人形机器人进行遥操作采集，捕捉了不同压力条件下的多模态交互。本工作亦旨在推动未来研究，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。
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