Abstract
Robots in the real world frequently come across identical objects in dense clutter. When evaluating grasp poses in these scenarios, a target-driven grasping system requires knowledge of spatial relations between scene objects (e.g., proximity, adjacency, and occlusions). To efficiently complete this task, we propose a target-driven grasping system that simultaneously considers object relations and predicts 6-DoF grasp poses. A densely cluttered scene is first formulated as a grasp graph with nodes representing object geometries in the grasp coordinate frame and edges indicating spatial relations between the objects. We design a Grasp Graph Neural Network (G2N2) that evaluates the grasp graph and finds the most feasible 6-DoF grasp pose for a target object. Additionally, we develop a shape completion-assisted grasp pose sampling method that improves sample quality and consequently grasping efficiency. We compare our method against several baselines in both simulated and real settings. In real-world experiments with novel objects, our approach achieves a 77.78% grasping accuracy in densely cluttered scenarios, surpassing the best-performing baseline by more than 15%. Supplementary material is available at https://sites.google.com/umn.edu/graph-grasping.
Original language | English (US) |
---|---|
Title of host publication | 2022 IEEE International Conference on Robotics and Automation, ICRA 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 742-748 |
Number of pages | 7 |
ISBN (Electronic) | 9781728196817 |
DOIs | |
State | Published - 2022 |
Externally published | Yes |
Event | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States Duration: May 23 2022 → May 27 2022 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
---|---|
ISSN (Print) | 1050-4729 |
Conference
Conference | 39th IEEE International Conference on Robotics and Automation, ICRA 2022 |
---|---|
Country/Territory | United States |
City | Philadelphia |
Period | 5/23/22 → 5/27/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Deep Learning in Grasping and Manipulation
- Grasping
- Perception for Grasping and Manipulation