Tactile sensing for stable object placing

Conference Article


Workshop on Touch Processing: a new Sensing Modality for AI at NeurIPS 2023 (WSTP)





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Placing objects on flat surfaces is a crucial skill to master for robots in house- hold environments. Common object-placing approaches require either complete scene specifications or (extrinsic) vision systems, which occasionally suffer from occlusions. Rather than relying on indirect measurements, we propose a novel approach for stable object placing that leverages tactile feedback from an object grasp. We devise a neural architecture called PlaceNet that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation. Our evaluation compares different sensing modalities to each other and PlaceNet to classical, non-learning approaches to assess whether a data-driven approach is indeed required. Applying PlaceNet to 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.


learning (artificial intelligence).

Author keywords

tactile sensing, object manipulation, object placing, neural networks

Scientific reference

L.M. Lach, N. Funk, R. Haschke, H. Ritter, J. Peters and G. Chalvatzaki. Tactile sensing for stable object placing, 2023 Workshop on Touch Processing: a new Sensing Modality for AI at NeurIPS 2023 , 2023, New Orleans (LA), USA, pp. 1-10.