Name
Title | ||
|---|---|---|
Vision-Based Imitation Learning Of Needle Reaching Skill For Robotic Precision Manipulation |
Abstract | ||
|---|---|---|
In this paper, an imitation learning approach of vision guided reaching skill is proposed for robotic precision manipulation, which enables the robot to adapt its end-effector's nonlinear motion with the awareness of collision-avoidance. The reaching skill model firstly uses the raw images of objects as inputs, and generates the incremental motion command to guide the lower-level vision-based controller. The needle's tip is detected in image space and the obstacle region is extracted by image segmentation. A neighborhood-sampling method is designed for needle component collision perception, which includes a neural networks based attention module. The neural network based policy module infers the desired motion in the image space according to the neighborhood-sampling result, goal and current positions of the needle's tip. A refinement module is developed to further improve the performance of the policy module. In three dimensional (3D) manipulation tasks, typically two cameras are used for image-based vision control. Therefore, considering the epipolar constraint, the relative movements in two cameras' views are refined by optimization. Experimental are conducted to validate the effectiveness of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10846-020-01290-1 | JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS |
Keywords | DocType | Volume |
Imitation learning, Skill learning, Visual control, Robotic precision manipulation, Neural networks | Journal | 101 |
Issue | ISSN | Citations |
1 | 0921-0296 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Li | 1 | 222 | 42.33 |
| Fangbo Qin | 2 | 11 | 4.78 |
| Shaofeng Du | 3 | 0 | 0.68 |
| De Xu | 4 | 142 | 25.04 |
| Jianqiang Zhang | 5 | 0 | 0.34 |