  This work deals with a practical everyday problem: stable object placement on
flat surfaces starting from unknown initial poses. Common object-placing
approaches require either complete scene specifications or extrinsic sensor
measurements, e.g., cameras, that occasionally suffer from occlusions. We
propose a novel approach for stable object placing that combines tactile
feedback and proprioceptive sensing. We devise a neural architecture that
estimates a rotation matrix, resulting in a corrective gripper movement that
aligns the object with the placing surface for the subsequent object
manipulation. We compare models with different sensing modalities, such as
force-torque and an external motion capture system, in real-world object
placing tasks with different objects. The experimental evaluation of our
placing policies with a set of unseen everyday objects reveals significant
generalization of our proposed pipeline, suggesting that tactile sensing plays
a vital role in the intrinsic understanding of robotic dexterous object
manipulation. Code, models, and supplementary videos are available at
https://sites.google.com/view/placing-by-touching.
