Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.18256 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912444567257088 |
|---|---|
| author | Jiang, Shuo Hu, Boce Zhao, Linfeng Wong, Lawson L. S. |
| author_facet | Jiang, Shuo Hu, Boce Zhao, Linfeng Wong, Lawson L. S. |
| contents | With the development of robot electronic skin technology, various tactile sensors, enhanced by AI, are unlocking a new dimension of perception for robots. In this work, we explore how robots equipped with electronic skin can recognize tactile gestures and interpret them as human commands. We developed a modular robot E-skin, composed of multiple irregularly shaped skin patches, which can be assembled to cover the robot's body while capturing real-time pressure and pose data from thousands of sensing points. To process this information, we propose an equivariant graph neural network-based recognizer that efficiently and accurately classifies diverse tactile gestures, including poke, grab, stroke, and double-pat. By mapping the recognized gestures to predefined robot actions, we enable intuitive human-robot interaction purely through tactile input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robot Tactile Gesture Recognition Based on Full-body Modular E-skin Jiang, Shuo Hu, Boce Zhao, Linfeng Wong, Lawson L. S. Robotics With the development of robot electronic skin technology, various tactile sensors, enhanced by AI, are unlocking a new dimension of perception for robots. In this work, we explore how robots equipped with electronic skin can recognize tactile gestures and interpret them as human commands. We developed a modular robot E-skin, composed of multiple irregularly shaped skin patches, which can be assembled to cover the robot's body while capturing real-time pressure and pose data from thousands of sensing points. To process this information, we propose an equivariant graph neural network-based recognizer that efficiently and accurately classifies diverse tactile gestures, including poke, grab, stroke, and double-pat. By mapping the recognized gestures to predefined robot actions, we enable intuitive human-robot interaction purely through tactile input. |
| title | Robot Tactile Gesture Recognition Based on Full-body Modular E-skin |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.18256 |