Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910444099207168 |
|---|---|
| author | Shan, Jianhua Sun, Yuhao Zhang, Shixin Sun, Fuchun Chen, Zixi Shen, Zirong Stefanini, Cesare Yang, Yiyong Luo, Shan Fang, Bin |
| author_facet | Shan, Jianhua Sun, Yuhao Zhang, Shixin Sun, Fuchun Chen, Zixi Shen, Zirong Stefanini, Cesare Yang, Yiyong Luo, Shan Fang, Bin |
| contents | Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07237 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor Shan, Jianhua Sun, Yuhao Zhang, Shixin Sun, Fuchun Chen, Zixi Shen, Zirong Stefanini, Cesare Yang, Yiyong Luo, Shan Fang, Bin Robotics Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method. |
| title | Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.07237 |