This work deals with a practical everyday prob- lem: 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 generaliza- tion of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation. Code, models, and supplemen- tary videos are available on https://sites.google.com/ view/placing-by-touching.
| Subject : | : | Poster only |
| Comment | : | I realize that this is a very technical submission from the field of robotics and is not directly connected to affective touch. However, it showcases typical uses of touch sensing in robotics, both for high-level tasks (object state estimation) and low-level control. This can form a good foundation for a discussion on how to incorporate affective touch into everyday robotic tasks and where it is most important. |
| Keywords | : | robotics ; machine learning ; force control ; object manipulation |
| PDF version | : |  PDF version |
