Learning-based Grasping and Manipulation in Robotics: Analyzing learning-based approaches for robotic grasping and manipulation tasks, including object recognition and pose estimation

Abstract

Robotic grasping and manipulation are crucial for the autonomy and versatility of robots in various applications, from manufacturing to household tasks. Traditional robotic grasping and manipulation approaches often rely on handcrafted algorithms, which can be challenging to adapt to diverse and complex environments. In recent years, there has been a growing interest in using learning-based approaches to address these challenges. This paper provides a comprehensive review and analysis of learning-based methods for robotic grasping and manipulation tasks, with a focus on object recognition and pose estimation. We discuss the key concepts, methodologies, and challenges in this field, as well as current trends and future directions.