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. Project Webpage: https://prg.cs.umd$.$edu/AcTExplore

翻译：触觉探索在理解物体结构中发挥着关键作用，对于机器人抓取和操控等基础任务至关重要。然而，使用触觉传感器高效探索此类物体具有挑战性，主要原因是未知环境规模庞大且这些传感器的感知覆盖范围有限。为此，我们提出了AcTExplore，一种由强化学习驱动的主动触觉探索方法，旨在以有限步数自动探索物体表面并实现规模化物体重建。通过充分的探索，我们的算法逐步收集触觉数据，同时重建物体的三维形状，这可作为更高层次下游任务的表征。我们的方法在仅用基本形状训练的情况下，在未见过的YCB物体上实现了平均95.97%的交并比覆盖率。项目网页：https://prg.cs.umd.edu/AcTExplore