Tactile exploration plays a crucial role in understanding object structures for fundamental robotics tasks such as grasping and manipulation. However, efficiently exploring such objects using tactile sensors is challenging, primarily due to the large-scale unknown environments and limited sensing coverage of these sensors. To this end, we present AcTExplore, an active tactile exploration method driven by reinforcement learning for object reconstruction at scales that automatically explores the object surfaces in a limited number of steps. Through sufficient exploration, our algorithm incrementally collects tactile data and reconstructs 3D shapes of the objects as well, which can serve as a representation for higher-level downstream tasks. Our method achieves an average of 95.97% IoU coverage on unseen YCB objects while just being trained on primitive shapes.

翻译：触觉探索在理解物体结构以完成抓取、操作等基础机器人任务中扮演着关键角色。然而，利用触觉传感器高效探索未知物体仍具挑战性，这主要源于大规模未知环境及传感器有限的感知覆盖范围。为此，我们提出AcTExplore——一种基于强化学习的主动触觉探索方法，能以有限步数自动探索物体表面，实现规模化物体重建。通过充分探索，本算法逐步收集触觉数据并同步重建物体的三维形状，该重建结果可作为高级下游任务的表征。在仅以原始形状训练的条件下，本方法在未见YCB物体上平均实现了95.97%的IoU覆盖率。