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This work deals with a practical everyday problem: stable object placement onflat surfaces starting from unknown initial poses. Common object-placingapproaches require either complete scene specifications or extrinsic sensormeasurements, e.g., cameras, that occasionally suffer from occlusions. Wepropose a novel approach for stable object placing that combines tactilefeedback and proprioceptive sensing. We devise a neural architecture thatestimates a rotation matrix, resulting in a corrective gripper movement thataligns the object with the placing surface for the subsequent objectmanipulation. We compare models with different sensing modalities, such asforce-torque and an external motion capture system, in real-world objectplacing tasks with different objects. The experimental evaluation of ourplacing policies with a set of unseen everyday objects reveals significantgeneralization of our proposed pipeline, suggesting that tactile sensing playsa vital role in the intrinsic understanding of robotic dexterous objectmanipulation. Code, models, and supplementary videos are available athttps://sites.google.com/view/placing-by-touching.

Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing