Name
Title | ||
|---|---|---|
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping |
Abstract | ||
|---|---|---|
Agile control of mobile manipulator is challenging because of the high complexity coupled by the robotic system and the unstructured working environment. Tracking and grasping a dynamic object with a random trajectory is even harder. In this paper, a multi-task reinforcement learning-based mobile manipulation control framework is proposed to achieve general dynamic object tracking and grasping. Several basic types of dynamic trajectories are chosen as the training set for the task. To improve policy generalization in practice, random noise and dynamics randomization are introduced during the training process. Extensive experiments show that our trained policy can adapt to unseen random dynamic trajectories with about 0.1 m tracking error and 75% grasping success rate for dynamic objects. The trained policy can also be successfully deployed on a real mobile manipulator. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACIRS55390.2022.9845515 | 2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) |
Keywords | DocType | ISBN |
Reinforcement Learning,Mobile Manipulation,Dynamic Object | Conference | 978-1-6654-8520-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Wang | 1 | 4463 | 204.50 |
| Qifeng Zhang | 2 | 0 | 1.35 |
| Xiao-Hui Wang | 3 | 39 | 6.73 |
| Shida Xu | 4 | 0 | 0.34 |
| Yvan Petillot | 5 | 0 | 0.34 |
| Sen Wang | 6 | 279 | 21.15 |