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.
References
Tatineni, Sumanth. "Blockchain and Data Science Integration for Secure and Transparent Data Sharing." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.3 (2019): 470-480.