Learning Human-Robot Interactions in Perturbed Teleoperations

Remotely controlling a robotic platform could be challenging, specifically in the presence of uncertainties between the human and robot. A major problem is that human’s inputs to the system may not meet all task-specific criteria. For example, in teleoperated medical robotic applications, disrupted haptic signals (force feedback) can make it difficult for the human operator to command precise maneuvers, preventing the robot from maintaining stable and safe task execution. This paper addresses this issue by proposing an imitation learning strategy through human-robot interactions. Learning from successful task executions, the proposed methodology can condition teleoperation signals and human operator’s inputs to determine the legibility of the concurrent execution of the task. Utilizing collections of structured demonstration data of successful scenarios, the current study incorporates the renowned deep learning technique, recurrent neural network with long short-term memory units to achieve the goal. The developed framework learns optimum policies from expert demonstrations through human interactions. Once properly learned, the framework mimics the accomplished executions to protect and guarantee the performance in the case of disrupted teleoperation signals. The results demonstrate the effectiveness of the developed framework in performing teleoperation tasks under uncertainty-perturbed scenarios, tested over a long distance between Australia and the USA. Additionally, the proposed learning-based strategy outperforms state-of-the-art teleoperation methods. The developed framework enables the human operator to achieve a zero failure ratio, a critical factor in safety-sensitive applications such as remote clinical diagnosis.