This work investigates the effectiveness of tactile sensing for the practical everyday problem of stable object placement on flat surfaces starting from unknown initial poses. 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, an external motion capture system, and two classical baseline models 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.
Niklas Funk,
Luca Lach,
Robert Haschke,
Severin Lemaignan,
Helge Joachim Ritter,
Jan Peters,
Georgia Chalvatzaki