Human supervisors in multi-robot systems are primarily responsible for monitoring robots, but can also be assigned with secondary tasks. These tasks can act as interruptions and can be categorized as either intrinsic, i.e., being directly related to the monitoring task, or extrinsic, i.e., being unrelated. In this paper, we investigate the impact of these two types of interruptions through a user study (N = 39), where participants monitor a number of remote mobile robots while intermittently being interrupted by either a robot fault correction task (intrinsic) or a messaging task (extrinsic). We find that task performance of participants does not change significantly with the interruptions but depends greatly on the number of robots. However, interruptions result in an increase in perceived workload, and extrinsic interruptions have a more negative effect on workload across all NASA-TLX scales. Participants also reported switching between extrinsic interruptions and the primary task to be more difficult compared to the intrinsic interruption case. Statistical significance of these results is confirmed using ANOVA and one-sample t-test. These findings suggest that when deciding task assignment in such supervision systems, one should limit interruptions from secondary tasks, especially extrinsic ones, in order to limit user workload.

This paper presents a solution for the problem of optimal planning for a robot in a collaborative humanrobot team, where the human supervisor is intermittently available to assist the robot in completing tasks more quickly. Specifically, we address the challenge of computing the fastest path between two configurations in an environment with time constraints on how long the robot can wait for assistance. To solve this problem, we propose a novel approach that utilizes the concepts of budget and critical departure times, which enables us to obtain optimal solution while scaling to larger problem instances than existing methods. We demonstrate the effectiveness of our approach by comparing it with several baseline algorithms on a city road network and analyzing the quality of the obtained solutions. Our work contributes to the field of robot planning by addressing a critical issue of incorporating human assistance and environmental restrictions, which has significant implications for real-world applications.

The use of collaborative robots is becoming more widespread across industries. This makes it essential to study robot planning in order to work effectively and smoothly with human teammates while maintaining a positive human perception of the robots. This paper evaluates the influence of a robot’s strategy and decision making on the participants’ perception of the robot. We designed an online experiment where a robot and participants need to collaborate and organize a set of objects. We studied three different strategies where the robot either prioritizes the human’s objective, its own objective, or uses a balanced strategy. We then analyze and report the results based on participants’ answers to questionnaires before and after the experiment, their comments, and their actions during the experiment. The results show that strategies prioritizing the human’s objective, or balancing between the robot’s and the human’s objectives can effectively improve participants’ perception of the robot and create a collaborative environment.

Estimating a user's expertise level based on observations of their actions will result in better human-robot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user it's interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The user's expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the “goodness” of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed after-task performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.

Compliant actuators are typically designed to possess a tunable positive stiffness characteristic in order to generate restoring force upon displacement. These actuators either require two independent motor units or closed-loop control to change both their equilibrium position and output stiffness. The introduction of negative stiffness, in combination with tunable positive stiffness, may reduce the complexity and extend the capability of these actuators in unexpected ways. In this paper, we present a compliant actuator that employs a passive negative stiffness mechanism in conjunction with an effectively tunable positive stiffness mechanism. We show that such actuator enables open-loop stiffness modulation and equilibrium position control using a single motor unit, as opposed to more conventional variable stiffness and series elastic actuators. The paper presents the theoretical foundation of positive-negative stiffness actuators and demonstrates low-power stiffness modulation and equilibrium position control achievable with a prototype positive-negative stiffness actuator.

Compliant actuators have found their place in areas of prosthetics, rehabilitation and robot locomotion because they enable safe human-robot and stable robot-environment interaction, both non-trivial to achieve using conventional rigid actuation. These actuators are capable of varying their equilibrium position and apparent output stiffness in a way humans change the resting position and compliance of their limbs. Just like antagonistically actuated human joints, these actuators require two motor units to provide control over the equilibrium position and the positive joint stiffness. Here we present a novel compliant actuation concept which affords control over the equilibrium position and joint stiffness using a single motor unit. In order to achieve this unconventional functionality, the actuator combines a passive positive feedback (negative stiffness) mechanism with an efficiently tunable negative feedback (positive stiffness) mechanism. This provides a novel design with two distinct operation modes, one leading to unprecedented stiffness tunability, while the other enabling equilibrium point controllability. We present the first practical implementation of this actuator using a prototype prosthetic limb design along with experimental data testifying the range of tunability, covering compliant to rigid behaviour, without paying much on the power input.

Energy efficient compliant actuation is the missing ingredient and key enabler of next-generation autonomous systems, domestic robots, prosthetic devices, orthotic devices, and wearable exoskeletons, to name a few. For all these devices, one would wish to develop actuators enabling wide range impedance modulation with low energy cost. Using conventional and biologically-inspired compliant actuation, previous research led to functional devices but with high energy cost. Here we introduce a minimalistic compliant actuator to realize impedance modulation with low energy cost. Using this actuator we demonstrate stiffness augmentation in human-machine collaboration. We argue that the non-biologically-inspired actuation concept presented here may effectively complement a biological system, by restoring or extending its functionality, with negligible energy cost.