Name
Abstract | ||
|---|---|---|
In a goal-conditioned grasping task, a robot is asked to grasp the objects designated by a user. Existing methods for goal-conditioned grasping either can only handle relatively simple scenes or require extra user annotations. This letter proposes an autonomous method to enable the grasping of target object in a challenging yet general scene that contains multiple objects of different classes. It can effectively learn a dense descriptor and integrate it with a newly designed grasp affordance model. The proposed method is a self-supervised pipeline trained without any human supervision or robotic sampling. We validate our method via both simulated and real-world experiments while the training relies only on a variety of synthetic data, demonstrating a good generalization capability. Supplementary video demonstrations and material are available at https://vsislab.github.io/agcg/. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2021.3062300 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Perception for Grasping manipulation, representation learning | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |