کنفرانس مهندسی برق ایران

صفحه اصلی / سی و دومین کنفرانس بین المللی مهندسی برق

Deep Learning-Based Imitation of Human Actions for Autonomous Pick-and-Place Tasks

نویسندگان :

Anoosheh Saadati¹ Mehdi Tale Masouleh² Ahmad Kalhor³

1- University of Tehran 2- University of Tehran 3- University of Tehran

کلمات کلیدی :

Imitation Learning،Autonomous Robotic Grasping،Path Tracking،Delta Parallel Robot،YOLOv8

چکیده :

In robotics, attempts have been made to imitate movements through learning from demonstrations. There are two types of demonstration-based learning. Expert-based learning is expensive and time-consuming since it necessitates a paired observation-action procedure. Conversely, visual-based learning is more practical to collect and makes robots more adaptable to many kinds of tasks. But this problem still needs to be answered completely, and it’s still a difficult assignment. This paper presents a comprehensive approach to replicating human actions in robots for object manipulation. As the main contribution of this paper, This research employs a deep learning methodology to allow robots to imitate human actions in grasping objects, utilizing human movement as a reference. A real dataset with 718 images and 142 videos has been collected in this paper. then The model is trained for object detection and object segmentation. To imitate human actions, a video of a person completing a task is recorded first. Following the subject’s movements in the video allows for the extraction of the motion path. The robot then proceeds along the path that was extracted. The DPR impressively demonstrates the practical application, showcasing its capacity to imitate and execute human actions.