Learning-based robotic grasping: A review
As personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as logistics, fast-moving consumer goods, and food delivery, these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without mu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-04-01
|
| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2023.1038658/full |
| _version_ | 1797852942452129792 |
|---|---|
| author | Zhen Xie Xinquan Liang Canale Roberto |
| author_facet | Zhen Xie Xinquan Liang Canale Roberto |
| author_sort | Zhen Xie |
| collection | DOAJ |
| description | As personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as logistics, fast-moving consumer goods, and food delivery, these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without much modification or downtime. Most solutions in the market are based on traditional object recognition and are, therefore, not suitable for grasping unknown objects with varying shapes and textures. Adequate learning policies enable robotic grasping to accommodate high-mix and low-volume manufacturing scenarios. In this paper, we review the recent development of learning-based robotic grasping techniques from a corpus of over 150 papers. In addition to addressing the current achievements from researchers all over the world, we also point out the gaps and challenges faced in AI-enabled grasping, which hinder robotization in the aforementioned industries. In addition to 3D object segmentation and learning-based grasping benchmarks, we have also performed a comprehensive market survey regarding tactile sensors and robot skin. Furthermore, we reviewed the latest literature on how sensor feedback can be trained by a learning model to provide valid inputs for grasping stability. Finally, learning-based soft gripping is evaluated as soft grippers can accommodate objects of various sizes and shapes and can even handle fragile objects. In general, robotic grasping can achieve higher flexibility and adaptability, when equipped with learning algorithms. |
| first_indexed | 2024-04-09T19:41:24Z |
| format | Article |
| id | doaj.art-ca6bd4b3a50b447d9e8859c5ec2849a7 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-04-09T19:41:24Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-ca6bd4b3a50b447d9e8859c5ec2849a72023-04-04T05:52:48ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442023-04-011010.3389/frobt.2023.10386581038658Learning-based robotic grasping: A reviewZhen Xie0Xinquan Liang1Canale Roberto2Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR), Singapore, SingaporeSingapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR), Singapore, SingaporeAdvanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR), Singapore, SingaporeAs personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as logistics, fast-moving consumer goods, and food delivery, these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without much modification or downtime. Most solutions in the market are based on traditional object recognition and are, therefore, not suitable for grasping unknown objects with varying shapes and textures. Adequate learning policies enable robotic grasping to accommodate high-mix and low-volume manufacturing scenarios. In this paper, we review the recent development of learning-based robotic grasping techniques from a corpus of over 150 papers. In addition to addressing the current achievements from researchers all over the world, we also point out the gaps and challenges faced in AI-enabled grasping, which hinder robotization in the aforementioned industries. In addition to 3D object segmentation and learning-based grasping benchmarks, we have also performed a comprehensive market survey regarding tactile sensors and robot skin. Furthermore, we reviewed the latest literature on how sensor feedback can be trained by a learning model to provide valid inputs for grasping stability. Finally, learning-based soft gripping is evaluated as soft grippers can accommodate objects of various sizes and shapes and can even handle fragile objects. In general, robotic grasping can achieve higher flexibility and adaptability, when equipped with learning algorithms.https://www.frontiersin.org/articles/10.3389/frobt.2023.1038658/fullversatile graspinglearning policyhigh mix and low volumepersonalizationtactile sensingsoft gripping |
| spellingShingle | Zhen Xie Xinquan Liang Canale Roberto Learning-based robotic grasping: A review Frontiers in Robotics and AI versatile grasping learning policy high mix and low volume personalization tactile sensing soft gripping |
| title | Learning-based robotic grasping: A review |
| title_full | Learning-based robotic grasping: A review |
| title_fullStr | Learning-based robotic grasping: A review |
| title_full_unstemmed | Learning-based robotic grasping: A review |
| title_short | Learning-based robotic grasping: A review |
| title_sort | learning based robotic grasping a review |
| topic | versatile grasping learning policy high mix and low volume personalization tactile sensing soft gripping |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2023.1038658/full |
| work_keys_str_mv | AT zhenxie learningbasedroboticgraspingareview AT xinquanliang learningbasedroboticgraspingareview AT canaleroberto learningbasedroboticgraspingareview |