Towards Explainable Shared Control using Augmented Reality Shared control plays a pivotal role in establishing effective humanrobot interactions. Traditional controlsharing methods strive to complement a humanxe2x80x99s capabilities at safely completing a task, and thereby rely on users forming a mental model of the expected robot behaviour. However, these methods can often bewilder or frustrate users whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. To resolve this model misalignment, we introduce Explainable Shared Control as a paradigm in which assistance and information feedback are jointly considered. Augmented reality is presented as an integral component of this paradigm, by visually unveiling the robotxe2x80x99s inner workings to human operators. Explainable Shared Control is instantiated and tested for assistive navigation in a setup involving a robotic wheelchair and a Microsoft HoloLens with addon eye tracking. Experimental results indicate that the introduced paradigm facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment.

Unified HumanRobot Shared Control with Application to Haptic Telemanipulation Humanrobot shared control (SC) has largely been studied in two complementary forms, namely divisible shared control (DSC) and interactive shared control (ISC). DSC enables clean division of the human and the robot subtasks, thus enabling them to work independently, while ISC allows for flexible intervention to improve the collaborative performance or experience. This paper presents a unified scheme that combines both forms of SCs to attain the benefits of flexibility as well as ease of use when human and robot jointly work on a task together. Based on the idea that flexibility should be embedded in every task constraint that the robot is controlling, we connect ISC into the robot subtask providing a soft boundary between the divided orthogonal subspaces allowing human to access the robot subtask and intervene whenever necessary. We also propose a new simple yet effective Cartesian stiffness adaptation law that enables the robot to modify its endpoint stiffness in the robotxe2x80x99s control subspace in the presence of disagreement from the human. Simulations and real robot studies for a teleoperated pathfollowing scenario were performed to demonstrate the flexibility of the unified shared control (USC), which allows the robot to dynamically adapt its task based on the operatorxe2x80x99s intentions.