Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks
Abstract
In the process of transportation, the handling and loading methods of rigid objects are becoming more and more perfect. However, whether in today’s transportation system or in daily life, such as packing objects or sorting cables before transportation, the manipulation of deformable objects has been always inevitable and has attracted more and more attention. Due to the super degrees of freedom and the unpredictable physical state of deformed objects. It is difficult for robots to complete tasks under the environment of the deformable object. Therefore, we present a method based on imitation learning. In the generated expert demonstration, the agent is offered to learn the state sequence, and then imitate the expert’s trajectory sequence which avoid the above-mentioned difficulties. In addition, compared with the baseline method, our proposed DN-Transporter Networks are more competitive in a simulation environment involving cloth, ropes or bags.
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Teng et al. achieved a 48.3% success rate out of 60 trials for item loading and sack moving in simulation.
“Teng et al. (2022) [238] achieved a 48.3% success rate out of 60 trials for item loading and sack moving in simulation.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [238]
The canonical task of bag manipulation is item loading which involves opening the bag, inserting items and lifting the bag.
“The canonical task of bag manipulation is item loading [238] which involves opening the bag, inserting items [34] (see Figure 8b) and lifting the bag.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [238]
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“During item insertion, the perception needs to reason whether the objects are within the region of open contour [238,242] of the bag.”
BackgroundInfluential
- Authors
- Teng, Y., Lu, H., Li, Y., Kamiya, T., Nakatoh, Y., Serikawa, S., & Gao, P.
- Affiliations
- Qingdao University (CN)
- Kyushu Institute of Technology (JP)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2023-04
- Volume
- 24
- Issue
- 4
- Pages
- 4532-4540
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W4226324806
- Cited by
- 15
- References
- 34
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 6 · 2023: 4 · 2022: 3
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (22K12094)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- deformable object manipulation, imitation learning, robotic manipulation, fabric manipulation, rope manipulation, simulation-based training
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