Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2303.00935 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910425286705152 |
|---|---|
| author | Hu, Xiaohai Venkatesh, Aparajit Wan, Yusen Zheng, Guiliang Jawale, Neel Kaur, Navneet Chen, Xu Birkmeyer, Paul |
| author_facet | Hu, Xiaohai Venkatesh, Aparajit Wan, Yusen Zheng, Guiliang Jawale, Neel Kaur, Navneet Chen, Xu Birkmeyer, Paul |
| contents | Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_00935 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy Hu, Xiaohai Venkatesh, Aparajit Wan, Yusen Zheng, Guiliang Jawale, Neel Kaur, Navneet Chen, Xu Birkmeyer, Paul Robotics Machine Learning Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented. |
| title | Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2303.00935 |