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

TL;DR

This study demonstrates that tactile sensing significantly enhances the accuracy and reliability of robotic object placement on flat surfaces, especially in scenarios with unknown initial poses and occlusions.

Contribution

The paper introduces a neural architecture that integrates tactile and proprioceptive sensing for stable object placement, showing improved generalization over existing methods.

Findings

Tactile sensing improves placement accuracy.

The proposed model generalizes well to unseen objects.

Combining tactile with other sensors enhances robustness.

Abstract

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.