  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
