Discrete NDimensional Entropy of Behavior: DNDEB Shared control for humanrobot teamsxe2x80x94where both the human and the robotxe2x80x99s autonomy provide commands to the hardwarexe2x80x94offers advantages over fully teleoperated or fully autonomous systems by utilizing the unique skill sets of both the human and robotxe2x80x99s autonomy simultaneously. However, the mechanism by which control is shared is often static and many teams could benefit from adjusting this mechanism, such that the human or autonomy alternatively receive more control authority in different scenarios. The question then is: how do we know when these scenarios occur? In this paper, we present a method to estimate the performance of humanrobot teams using a novel metric called Discrete NDimensional Entropy of Behavior (DNDEB). DNDEB utilizes knowledge of a highperforming humanrobot team to build a model of how the team should operate. The model is used to predict the humanxe2x80x99s command. The error between the prediction and actual command is tracked and after a certain number of samples, entropy is estimated. A higher level of entropy corresponds to deviations from the highperformance model, which can be interpreted as poor performance by the humanrobot team (e.g., long task time or a collision). Our formulation offers several advantages: it (1) accepts discrete inputs of any size, (2) does not require additional sensors, and (3) is tunable to the specific application. To validate this, we conduct a 15person study where subjects operated a powered wheelchair under three different sharedcontrol paradigms. We find that entropy is higher for cases with longer task durations and cases where there is a collision. Moreover, we use DNDEB thresholds as a mechanism to predict the performance of the humanrobot team online and find an average accuracy of 91% with a prescience rate of 72%.

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.