To Örebro University

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human-robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human-robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human-robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

Failures can occur in any interaction between humans and robots, and the extent of the failure depends on the domain of application and the context in which the failure occurs. Typically, interaction failures are attributed to errors on the robot’s side. This paper examines the concept of Intelligent Disobedience (ID) and how it can be leveraged for managing interaction failures caused by humans. Our work-in-progress focuses on the practical implementation of ID in robot teleoperation and highlights the benefits of interdisciplinary collaboration for this project.

Failures are natural and unavoidable events in any form of interaction, especially in human-robot interactions (HRI). Throughout the literature, the definition and classification of failures are diverse, depending on the source and application domain. However, the tolerance to the aftereffect of these failures is low in teleoperation due to its unstructured application domains. One such type of failure is called human induced interaction failure. This is an interesting and often overlooked failure type, due to the perspective that robots are designed always to obey the instructions given by the human operators. Regardless of the degree of automation that the robot is equipped with. But what if the instructions provided are faulty, dangerous, or misleading. This paper addresses the above mentioned research gap. It introduces a framework based on the concept of Intelligent Disobedience (ID), derived from guide dog training methods, to manage human induced interaction failures in teleoperation scenarios.

Human-robot interaction in any form and application domains are susceptible to failures. Failures are of varied definitions and types. The severity of the failure is high for teleoperated tasks. In this paper, we focus on the less explored type of failure called human induced interaction failure using the concept of Intelligent Dis-obedience (ID). ID was first used as a method for training guide dogs, where the dogs disobey the user’s commands that are dangerous to perform. Imparting this behaviour of disobedience to manage human induced interaction failures consists of numerous social, cultural and ethical aspects to consider. This paper discusses a novel framework based on ID to manage human induced interaction failures. Also, we discuss a nuanced approach involved in robot disobedience considering the different social and cultural aspects.

Remote teleoperation using a Virtual Reality (VR) allows users to experience better degrees of immersion and embodiment. Equipped with a variety of sensors, VR headsets have the potential to offer automatic adaptation to users' personal preferences and modes of operation. However, to achieve this goal VR users must be uniquely identifiable. In this paper, we investigate the possibility of identifying VR users teleoperating a simulated robotic arm, by their forms of interaction with the VR environment. In particular, in addition to standard head and eye data, our framework uses hand tracking data provided by a Leap Motion hand-tracking sensor. Our first set of experiments shows that it is possible to identify users with an accuracy close to 100% by aggregating the sessions data and training/testing with a 70/30 split approach. Last, our second set of experiments show that, even by training and testing on separated sessions, it is still possible to identify users with a satisfactory accuracy of 89,23%.

The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.

Caregivers in eldercare can benefit from telepresence robots that allow them to perform a variety of tasks remotely. In order for such robots to be operated effectively and efficiently by non-technical users, it is important to examine if and how the robotic system's level of automation (LOA) impacts their performance.  The objective of this work was to develop suitable LOA modes for a mobile robotic telepresence (MRP) system for eldercare and assess their influence on users' performance, workload, awareness of the environment and usability at two different levels of task complexity. For this purpose, two LOA modes were implemented on the MRP platform: assisted teleoperation (low LOA mode) and autonomous navigation (high LOA mode). The system was evaluated in a user study with 20 participants, who, in the role of the caregiver, navigated the robot through a home-like environment to perform control and perception tasks. Results revealed that performance improved in the high LOA when task complexity was low. However, when task complexity increased, lower LOA improved performance. This opposite trend was also observed in the results for workload and situation awareness. We discuss the results in terms of the LOAs' impact on users' attitude towards automation and implications on usability.

Probiotics are suggested to impact physiological and psychological stress responses by acting on the gut-brain axis. We investigated if a probiotic product containing Bifidobacterium longum R0175, Lactobacillus helveticus R0052 and Lactiplantibacillus plantarum R1012 affected stress processing in a double-blinded, randomised, placebo-controlled, crossover proof-of-concept study (NCT03615651). Twenty-two healthy subjects (24.2 ± 3.4 years, 6 men/16 women) underwent a probiotic and placebo intervention for 4 weeks each, separated by a 4-week washout period. Subjects were examined by functional magnetic resonance imaging while performing the Montreal Imaging Stress Task (MIST) as well as an autonomic nervous system function assessment during the Stroop task. Reduced activation in regions of the lateral orbital and ventral cingulate gyri was observed after probiotic intervention compared to placebo. Significantly increased functional connectivity was found between the upper limbic region and medioventral area. Interestingly, probiotic intervention seemed to predominantly affect the initial stress response. Salivary cortisol secretion during the task was not altered. Probiotic intervention did not affect cognitive performance and autonomic nervous system function during Stroop. The probiotic intervention was able to subtly alter brain activity and functional connectivity in regions known to regulate emotion and stress responses. These findings support the potential of probiotics as a non-pharmaceutical treatment modality for stress-related disorders.

Background: Evidence from preclinical studies suggests that probiotics affect brain function via the microbiome-gut-brain axis, but evidence in humans remains limited.

Objective: The present proof-of-concept study investigated if a probiotic product containing a mixture of Bifidobacterium longum R0175, Lactobacillus helveticus R0052 and Lactiplantibacillus plantarum R1012 (in total 3 × 10⁹ CFU/day) affected functional brain responses in healthy subjects during an emotional attention task.

Design: In this double-blinded, randomized, placebo-controlled crossover study (Clinicaltrials.gov, NCT03615651), 22 healthy subjects (24.2 ± 3.4 years, 6 males/16 females) were exposed to a probiotic intervention and a placebo for 4 weeks each, separated by a 4-week washout period. Subjects underwent functional magnetic resonance imaging while performing an emotional attention task after each intervention period. Differential brain activity and functional connectivity were assessed.

Results: Altered brain responses were observed in brain regions implicated in emotional, cognitive and face processing. Increased activation in the orbitofrontal cortex, a region that receives extensive sensory input and in turn projects to regions implicated in emotional processing, was found after probiotic intervention compared to placebo using a cluster-based analysis of functionally defined areas. Significantly reduced task-related functional connectivity was observed after the probiotic intervention compared to placebo. Fecal microbiota composition was not majorly affected by probiotic intervention.

Conclusion: The probiotic intervention resulted in subtly altered brain activity and functional connectivity in healthy subjects performing an emotional task without major effects on the fecal microbiota composition. This indicates that the probiotic effects occurred via microbe-host interactions on other levels. Further analysis of signaling molecules could give possible insights into the modes of action of the probiotic intervention on the gut-brain axis in general and brain function specifically. The presented findings further support the growing consensus that probiotic supplementation influences brain function and emotional regulation, even in healthy subjects. Future studies including patients with altered emotional processing, such as anxiety or depression symptoms are of great interest.

Clinical Trial Registration: [http://clinicaltrials.gov/], identifier [NCT03615651].

Virtual Reality (VR) is regarded as a useful tool for teleoperation system that provides operators an immersive visual feedback on the robot and the environment. However, without any haptic feedback or physical constructions, VR-based teleoperation systems normally have poor maneuverability and may cause operational faults in some fine movements. In this paper, we employ Mixed Reality (MR), which combines real and virtual worlds, to develop a novel teleoperation system. New system design and control algorithms are proposed. For the system design, a MR interface is developed based on a virtual environment augmented with real-time data from the task space with a goal to enhance the operator’s visual perception. To allow the operator to be freely decoupled from the control loop and offload the operator’s burden, a new interaction proxy is proposed to control the robot. For the control algorithms, two control modes are introduced to improve long-distance movements and fine movements of the MR-based teleoperation. In addition, a set of fuzzy logic based methods are proposed to regulate the position, velocity and force of the robot in order to enhance the system maneuverability and deal with the potential operational faults. Barrier Lyapunov Function (BLF) and back-stepping methods are leveraged to design the control laws and simultaneously guarantee the system stability under state constraints.  Experiments conducted using a 6-Degree of Freedom (DoF) robotic arm prove the feasibility of the system.