Toward Improving Task-Level Commanding in Space Robotics Teleoperation Through Shared Mental Models

Kurzfassung

Human involvement in space missions is inherently limited by safety concerns and extreme environmental conditions. Therefore, exploration and infrastructure maintenance in space require the teleoperation of robotic systems with various capabilities. To support the user in achieving teleoperation goals under challenging conditions, such as limited bandwidth and communication latency, a multimodal user interface (UI) and a scalable autonomy commanding concept are needed. This approach allows the operator to command the robot using different input modalities, including joystick and force-feedback devices for direct teleoperation, as well as a graphical user interface (GUI) for task-level commands. Complex robotic systems offer a wide variety of task-level commands, making it difficult for the user to select the optimal command to achieve a given task goal. Enabling the robot to build a Shared Mental Model (SMM) with the operator helps to overcome this challenge. An SMM refers to a shared and compatible mental representation that humans and robots hold as members of a team, encompassing information about the common task goal (task model) and the skills and intentions of team members (user model). SMMs are associated with improved task performance and reduced mental workload for the operator in human-robot teleoperation. Since the robot initially lacks knowledge about the task goal and cannot directly create an SMM, we design a framework that enables the robot to estimate the user's (sub-)task goal based on a priori knowledge of the user's task execution behavior. We integrate the a priori knowledge into a Bayesian estimation framework, combining it with information about user input and the robot's world state in the remote environment. Based on this, the robot estimates the user's subtask goal and determines the task-level command best suited to achieve it. This command is then recommended to the user. To evaluate the prediction accuracy of our framework, we use data from the International Space Station (ISS)-toground telerobotic experiments conducted in the Surface Avatar technology demonstration mission. We compare the task-level commands selected by the user with the predictions of our framework, as well as with a sequence of task-level commands for efficiently achieving the subtask goal that an expert user would choose. Furthermore, we analyze how such an SMM can support learning conduciveness and offer an outlook on how it may contribute to mutual learning in a fully sociotechnical system.