Dexterous robotic manipulation using deep learning based on coupled RGBD tactile sensory data

Advancements in robotics have spurred the need for autonomous manipulation in intricate, entangled environments. Traditional systems face limitations in perception, hindering dexterous manipulation. To overcome this, the proposed project will integrate deep learning with coupled RGBD and tactile sensory data. By leveraging deep learning algorithms, meaningful representations can be extracted from sensory inputs, enhancing the robot's understanding and interaction with its surroundings. Extensive experimentation will validate the approach in terms of enabling precise manipulation in complex environments. This research project paves the way for applications in warehouse automation, manufacturing, healthcare, and agriculture, ushering in a new era of robotic capabilities.