Formulation and Validation of an Intuitive Quality Measure for Antipodal Grasp Pose Evaluation
| Autor: | Rajiv Dubey, Sudeep Sarkar, Redwan Alqasemi, Tian Tan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
Control and Optimization business.industry Computer science Mechanical Engineering Deep learning media_common.quotation_subject GRASP Biomedical Engineering Measure (physics) Computer Science Applications Visualization Human-Computer Interaction Artificial Intelligence Control and Systems Engineering Grippers Robot Computer vision Quality (business) Computer Vision and Pattern Recognition Artificial intelligence Set (psychology) business media_common |
| Zdroj: | IEEE Robotics and Automation Letters. 6:6907-6914 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2021.3096192 |
| Popis: | This letter describes a novel grasp quality measure that we developed for evaluating antipodal grasp poses in real-time. To quantify the grasp quality, we compute a set of object movement features from analyzing the interaction between the gripper and the object's projections in the image space. The normalization and weights of the features are tuned to make practical and intuitive grasp quality predictions. To evaluate our grasp quality measure, we conducted a real robot grasping experiment with 1000 robot grasp trials on 10 household objects to examine the relationship between our grasp scores and the actual robot grasping results. The results show that the average grasp success rate increases, and the average amount of undesired object movement decreases as the calculated grasp score increases. We achieved a 100% grasp success rate from 100 grasps of the 10 objects when using our grasp quality measure in planning top quality grasps. In addition, we compared our quality measure with the Q measure and deep learning-based quality measures. |
| Databáze: | OpenAIRE |
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