کنفرانس مهندسی برق ایران

صفحه اصلی / سی و دومین کنفرانس بین المللی مهندسی برق

Explorable Grasp Pose Detection for Two-Finger Robot Handover

نویسندگان :

AliReza Beigy¹ Mehdi Tale Masouleh² Ahmad Kalhor³

1- دانشگاه تهران 2- دانشگاه تهران 3- دانشگاه تهران

کلمات کلیدی :

3D grasp pose detection،Optimal grasping regions،grasp quality metrics،Geometry،Annotated datasets

چکیده :

The efficient generation of datasets for deep neural networks is crucial for enhancing their performance, especially in complex tasks like grasp pose detection in robotic grasp planning. Traditional methods often resort to random selection or rely on human-annotated datasets, making it challenging for networks to accurately learn graspable areas. For 3D grasp pose detection, an exhaustive search within an expansive space of points consumes substantial time and often overlooks an object's potential graspable regions. This paper introduces an algorithm capable of leveraging depth sensor information, particularly from devices like Kinect, to precisely determine the best grasp poses for object handovers. The proposed method, named Explorable Grasp Detection is based on the idea of exploitation and exploration, ensuring there is no redundant grasp detection within the same region. The results derived from DexNet's mesh objects dataset indicate remarkable computational efficiency, achieving grasp results in less than one second for various objects and maintaining a high success rate. Furthermore, error functions as geometry grasp quality metrics tailored for a two-finger gripper are incorporated to evaluate the grasp quality. One of the most notable contributions of this paper is the algorithm's ability to not only detect optimal grasping regions rapidly but also to serve as an instrumental tool for generating annotated datasets, thereby pushing forward the boundaries in robotic object manipulation techniques.