Data-Driven Teleoperation with Deep Principal Component Analysis

Teleoperation refers to the remote control of robots or machines by a human operator, often from a distance. It is widely used in various fields, including surgery, space exploration, and hazardous environments, where direct human involvement is difficult or dangerous. The significance of teleoperation lies in its ability to extend human capability, enabling precise, real-time manipulation in areas beyond human reach. However, teleoperation faces several challenges. One major difficulty is the complexity of controlling robotic systems with high degrees of freedom, where multiple joints or axes of movement need to be synchronized. The mismatch between the user's control input (such as from a joystick) and the robot's required precision and flexibility adds to the challenge. The human operator often lacks the capacity to intuitively manage the robot's full range of motion, which can result in reduced accuracy and task efficiency. To address these difficulties, a novel data-driven method is needed. Traditional teleoperation systems struggle with optimizing control schemes, especially when the user's input device cannot fully match the robot's capabilities. A data-driven approach that adapts to the specific control requirements of the task and robot configuration can bridge this gap by providing more intelligent, responsive control mechanisms. This thesis presents a new method for teleoperating high degree robotic manipulators with low dimensional controller like a joystick. The proposed approach combines deep probabilistic principal component analysis (PPCA) with mode switching to effectively map joystick actions to the robot's joint velocities. By integrating intelligent techniques, the system enhances the user's control over the robot's end-effector, improving performance and usability in complex teleoperation tasks.