面向接触丰富操作任务的人形机器人视觉-触觉-动作数据集
A Humanoid Visual-Tactile-Action Dataset for Contact-Rich Manipulation
AI总结：
针对现有机器人操作数据集对软体物体、多压力场景触觉数据覆盖不足的问题，构建了遥操作采集的人形机器人多模态视觉-触觉-动作数据集，支撑相关模型优化研究。
摘要：
接触丰富的操作在机器人学习领域的重要性日益提升，但此前机器人学习数据集相关研究多聚焦刚性物体，对真实操作场景下压力条件的多样性覆盖不足。为填补该空白，本文提出一款专为可变形软体物体操作设计的人形机器人视觉-触觉-动作数据集。该数据集由搭载灵巧手的人形机器人通过遥操作采集，捕获了不同压力条件下的多模态交互信息。本研究也为后续研发可有效利用触觉信号复杂性与多样性、具备先进优化策略的模型提供了方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
