HapTile: 用于接触丰富模仿学习的触觉感知视觉-触觉-语言-动作数据集
HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning
AI总结：
提出HapTile数据集，通过集成指尖触觉反馈和操作员触觉感知，为接触丰富的机器人操作任务提供视觉-触觉-语言-动作联合数据，并验证其在策略学习中的有效性。
摘要：
尽管触觉感知对于可靠操作至关重要，但大多数现有的视觉-语言-动作（VLA）数据集仍然仅基于视觉，而那些确实包含触觉信息的数据集通常缺乏任务多样性、语言条件和动作轨迹的联合组合。此外，现有的遥操作流程很少为操作员提供触觉反馈，尽管触觉反馈在演示质量和操作稳定性中具有公认的作用。在这项工作中，我们提出了HapTile，一个接触基础的视觉触觉操作数据集，它通过嵌入两个层次的物理交互感知超越了仅视觉轨迹数据集：机器人末端执行器上的指尖触觉反馈，以及遥操作侧的触觉感知演示。数据收集平台将触觉反馈直接集成到遥操作控制器中，使操作员能够实时感知接触交互。它基于一个标准且可复现的机器人系统构建，该系统配备了定制设计的指尖触觉传感器。该数据集涵盖了日常操作任务，包括拾取与放置、折叠、按压、堆叠以及其他常规活动，这些任务涉及广泛的接触丰富技能。每个任务都配有语言指令，用于根据操作目标对策略进行条件化，同时还有同步的视觉触觉观察和动作轨迹。此外，我们使用两个基线模型对接触丰富的策略学习进行了基准研究，以评估所提出的接触基础数据集的有效性。数据集和更多详细信息可在我们的网站上获取：haptile-dataset.github.io。
英文摘要：
Despite the importance of tactile sensing for reliable manipulation, most existing Vision-Language-Action (VLA) datasets remain vision-only, and those that do incorporate tactile information typically lack the joint combination of task diversity, language conditioning, and action trajectories. Furthermore, existing teleoperation pipelines rarely provide haptic feedback to the operator, despite its established role in demonstration quality and manipulation stability. In this work, we present HapTile, a contact-grounded visuotactile manipulation dataset that advances beyond vision-only trajectory datasets by embedding physical interaction sensing at two levels: fingertip tactile feedback at the robot end-effector, and haptic-informed demonstrations at the teleoperator side. The data collection platform integrates haptic feedback directly into the teleoperation controller, enabling the operator to perceive contact interactions in real time. It is built around a standard and reproducible robotic system equipped with custom-designed fingertip tactile sensors. The dataset comprises everyday manipulation tasks spanning a broad range of contact-rich skills, including pick-and-place, folding, pressing, stacking, and other routine activities. Each task is paired with language instructions that condition the policy on the manipulation objective, together with synchronized visuotactile observations and action trajectories. In addition, we provide a benchmarking study on contact-rich policy learning using two baseline models to evaluate the effectiveness of the proposed contact-grounded dataset. The dataset and additional details are available on our website: haptile-dataset.github.io.
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。
