Vision-Guided Grasping Policy Learning from Demonstrations for Robotic Manipulators

Abstract

The integration of robotics into domestic environments poses significant challenges due to the dynamic and varied nature of these settings. This paper introduces a new framework that combines vision-guided object recognition with adaptive grasping policies learned from human demonstrations. By harnessing computer vision technology, our system employs deep learning algorithms, particularly Convolutional Neural Networks (CNNs), to precisely detect and classify household objects. Simultaneously, the system uses imitation learning to refine grasping policies, enabling the robotic manipulator to dynamically adapt to new target objects. We validated our framework through a series of experimental setups that simulate typical kitchen tasks, such as manipulating utensils and preparing ingredients. These tasks, which primarily involve picking up and placing objects, served as practical tests for our system. The results demonstrate the system’s ability to effectively recognize a broad array of objects and adapt its grasping policies, thereby enhancing operational efficiency.