Service robots are becoming a common sight in hospital corridors in many developed countries, but their acceptance, the factors influencing it, and potential cultural differences between countries remain unclear. To investigate this, the authors examined the use of service robots in two hospitals, one in Denmark and the other in South Korea, through an exploratory study. The analysis was performed based on retrospective data including usage parameters and a survey was conducted to assess the attitudes of healthcare personnel toward blood sample and medication delivery robots. The survey found that the staff in both hospitals were positive regarding the deployment of such robots. Additionally, the results indicate that the individualization of robots may lead to higher agency and that swift removal of technical issues at deployment might positively influence longer-term satisfaction with the robots.

In human-robot collaboration scenarios, mutual adaptation between the human and robot must occur to ensure high task performance. This requires robotic systems to be capable of reasoning based on a long-term history of interactions. In this paper, we present and evaluate a robot simulation system that facilitates adaptive robot behavior using ontology-based reasoning and behavior trees in an interactive robotic scanning task. A study with 38 participants compares our adaptive system with a static system in team performance and perceived system usability. Our results suggest that use of the adaptive system significantly reduced session time, leading users to perform the task 19.5% faster. Furthermore, participants reported significantly lower fatigue levels, while maintaining the same task performance as those using the static system.

Mobile robots are increasingly deployed in various applications, including autonomous vehicles and logistics. Conflict-Based Search (CBS) is a promising approach for Multi-Agent Path Finding (MAPF), but has limitations when applied to real-world scenarios. This paper explores the challenges of adapting CBS to real-world mobile robotics, focusing on additional conflicts caused by imperfect navigation. We propose an Adaptive Conflict Detection (ACD) approach that proactively identifies conflicts within a rolling time window, making CBS more suitable for real-world applications. Both virtual and real robots are used to evaluate the importance of an adaptation to CBS if adapted to real scenarios. Experimental results show that ACD outperforms traditional CBS when penalties for conflict resolution are applied, demonstrating its potential for improved performance and reliability in practical multi-agent path planning applications.

Given a setup with external cameras and a mobile manipulator with an eye-in-hand camera, we address theproblem of computing a sequence of base poses and grasp choices that allows for clearing objects from atable while minimizing the overall execution time. The first step in our approach is to construct a worldmodel, which is generated by an anchoring process, using information from the external cameras. Next, wedeveloped a planning module which – based on the contents of the world model - is able to create a plausibleplan for reaching base positions and suitable grasp choices keeping execution time minimal. Comparing ourapproach to two baseline methods shows that the average execution cost of plans computed by our approach is40% lower than the naive baseline and 33% lower than the heuristic-based baseline. Furthermore, we integrateour approach in a demonstrator, undertaking the full complexity of the problem.

This paper communicates findings from an ethnographic inspired field study of human-robot teamwork in a hospital, a highly significant topic, as the use of robots has expanded significantly in recent years, and they are being increasingly deployed in naturalistic environments, including hospitals, expected to take part in socio-technical practices and collaborate with humans in teams. The field study took place in a Danish hospital where mobile robots were installed to take on courier tasks and identified two primary human-robot teams in the given setting: one team consisting of the hospital’s Technical Manager and the mobile robots and another team consisting of Medical Laboratory Technicians and the mobile robots. The team comprising Medical Laboratory Technicians had a strong dependency on the team encompassing the Technical Manager, in the daily hospital operations. In addition, two main elements affected the teamwork between hospital staff and mobile robots in the given hospital. First, a clear division of responsibility for the robots, including well-defined, simple tasks and instant troubleshooting, was important in ensuring collaborative teamwork. Second, environmental factors were crucial as the hospital setting must be suited for both staff and robots, for the teamwork to succeed. The results were evaluated in comparison to results in a similar, earlier study conducted at another Danish hospital and consequently reveal how a clear division of responsibility for robots and appropriate environmental infrastructure allows for the teamwork between humans and robots to flow satisfactory.

Due to demographic change, health and elderly care systems are facing a shortage of qualified caregivers. This issue can be addressed by introducing welfare robots into people’s homes, hospitals, and care institutions. To provide useful support, such robots must adapt to individual users and smoothly interact with them. From this perspective, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: 1) navigation control; 2) visual human detection; and 3) temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments. At the same time, it can predict human walking speed based on visual information without prior knowledge of personality and preferences (i.e., walking speed). The robot then uses this prediction to continuously adapt its speed to individual users in a proactive online manner. We validate the performance of the proposed proactive robot control in different real-world environments with various users, including an elderly resident of a Danish elderly care center. The results show that the robot successfully and smoothly guided various users of different ages and average walking speeds (e.g., 0.2 m/s, 0.7 m/s, and 1.1 m/s) to target locations over distances of 25–60 m. All in all, this study captures a wide range of research from robot control technology development to technological validity in a relevant environment and system prototype demonstration in an operational environment (i.e., an elderly care center).

Our everyday living environment is created for people. When mobile robots negotiate this space, they might get stuck on hard to solve obstacles thus requiring people's help. The robot will need to persuade passers-by to assist it in overcoming these obstacles to reduce the need for maintenance interventions, which can be costly. In our study, we enabled a mobile robot to communicate its need for assistance in multiple ways, including beeping, synthesized voice and movement. These behaviors were tested in combination, to ascertain which is the most effective. We found that the robot which communicates being stuck using movement and voice gets the most help, while static, beeping and silent robots get much less help. However, subjective data shows that people might feel even more empathy towards the robot expressing its problem by beeping compared to using spoken messages.

With the emergence of robots being deployed in unstructured environments outside the industrial domain, the importance of robots behaving appropriately in the vicinity of people is becoming more clear. These behaviours are hard to model as they depend on the social context. This context includes among other things where the robot is deployed, how crowded that place is, as well as who are residing in that place. In this paper we extend social space theory with the social context, making them adaptable to the current situation. We implement the social spaces as costmaps used in the standard ROS navigation stack. Our method - Context-Aware Social robot Navigation (CASN) - is tested in the context of people avoidance in social navigation. We compare CASN with the social navigation layer package, which also implements costs based on detected people. We show that by using CASN a mobile robot complies with social conventions in four different navigation scenarios.

Robot navigation in environments shared with humans should take into account social structures and interactions. The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups (FSCS), utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.

In this paper, we present a method for proactive approaching of humans for human-robot cooperative tasks such as a robot serving beverages to people. The proposed method can deal robustly with the uncertainties in the robot’s perception while also ensuring socially acceptable behavior. We use multiple modalities in the form of the robot’s motion, body orientation, speech and gaze to proactively approach humans. Further, we present a behavior tree based control architecture to efficiently integrate these different modalities. The proposed method was successfully integrated and tested on a beverage serving robot. We present the findings of our experiments and discuss possible extensions to address limitations.

Hand disinfection in public spaces is of great importance in preventing infectious diseases. However not everyone sanitizes their hands using hand sanitizer dispensers, and even if they do, many of them don’t rub hands for a long enough time for the sanitizing agent to become most effective. For these reasons we designed a robotic interface for automatic hand sanitizer dispensers to motivate people to disinfect their hands more often and for a longer time. We use interactive elements like speech and gaze communication to achieve this. In our in-the-wild studies we have found that using our system resulted in 21% more hand sanitizations and a much longer hand rubbing time, which in turn leads to better public hygiene and better prevention of infectious diseases.

In this paper, we propose an end-to-end-solution to the problem of multi-object tracking on a mobile robot. The tracking system consists of a process where we project 2D multi-object detections to the robots base frame, using RGB-D sensor data. These detections are then transformed to the map frame using a localization algorithm. This system predicts trajectories of humans and objects in the environment of the robot and can be adapted to work with any detector and track from multiple cameras. The system can then be used to build a temporally consistent costmap to improve navigation strategies.

In this article, we address to what extent the proverb “the sound makes the music” also applies to human-robot interaction, and whether robots could profit from using speech characteristics similar to those used by charismatic speakers like Steve Jobs. In three empirical studies, we investigate the effects of using Steve Jobs’ and Mark Zuckerberg's speech characteristics during the generation of robot speech on the robot's persuasiveness and its impressionistic evaluation. The three studies address different human-robot interaction situations, which range from online questionnaires to real-time interactions with a large service robot, yet all involve both behavioral measures and users’ assessments. The results clearly show that robots can profit from using charismatic speech.

In this paper, we address issues of timing between robot behaviors in multimodal human-robot interaction. In particular, we study what effects sequential order and simultaneity of robot arm and body movement and verbal behavior have on the fluency of interactions. In a study with the Care-O-bot, a large service robot, in a medical measurement scenario, we compare the timing of the robot's behaviors in three between-subject conditions. The results show that the relative timing of robot behaviors has significant effects on the number of problems participants encounter, and that the robot's verbal output plays a special role because participants carry their expectations from human verbal interaction into the interactions with robots.

A general requirement for any service robot is to be flexible and capable of processing uncertainties, thus making it adaptable for multiple tasks. As a result, learning the appropriate action parameters for a specific action is a crucial task. The method presented in this paper is an incremental statistical learning method that takes into consideration the uncertainties and the contact forces to find the optimal parameter sets. The method is inspired by solutions available in industrial robotics and it uses a dynamic simulator and Kernel Density Estimation in order to find the parameter sets that lead to a successful Peg-in-Hole action. The solution obtained in the simulation is successfully tested on a real service robot.

In this paper, we explore experimentally the possible tradeoff between gaze to the user and gaze to the path in robot approach. While some previous work indicates that gaze towards the user increases perceived safety because the user feels recognized, other work indicates that it is legibility of the robot's actions that put users at ease. If the robot does not drive up to the person in a straight line directly, the robot can either continuously look at the person and thus maintain eye contact, or indicate its path through its gaze behavior, increasing legibility. In an experiment with N=36 participants, we tested the tradeoff between legibility and eye contact. The behavioral results show that users are significantly more at ease with the robot that gazes at them than with the robot that looks where it is going, measured by the number of instances of glances away from the robot. Likewise, the participants rate the robot that looks at them continuously as more intelligent and more cooperative. Thus, participants value mutual gaze higher than legibility.

In this paper we present an object detection method that uses edge categorisation in combination with a local multi-modal histogram descriptor, all based on RGB-D data. Our target application is robust detection and pose estimation of known objects. We propose to apply a recently introduced edge categorisation algorithm for describing objects in terms of its different edge types. Relying on edge information allow our system to deal with objects with little or no texture or surface variation. We show that edge categorisation improves matching performance due to the higher level of discrimination, which is made possible by the explicit use of edge categories in the feature descriptor. We quantitatively compare our approach with the state-of-the-art template based Linemod method, which also provides an effective way of dealing with texture-less objects, tests were performed on our own object dataset. Our results show that detection based on edge local multi-modal histogram descriptor outperforms Linemod with a significantly smaller amount of templates.

This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g, for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape. Note to Practitioners - The aim of this work was to facilitate the setup of robot-based automation of delicate handling of flexible objects consisting of a uniform material. As examples, we have considered how to optimally maneuver flexible objects through a hole without colliding and how to place flexible objects on a flat surface with minimal introduction of internal stresses in the object. Given the material properties of the object, we have demonstrated in these two applications how the system can be programmed with minimal requirements of human intervention. Rather than being an integrated system with the drawbacks in terms of lacking flexibility, our system should be viewed as a library of new technologies that have been proven to work in close to industrial conditions. As a rather basic, but necessary part, we provide a technology for determining the shape of the object when passing on, e.g., a conveyor belt prior to being handled. The main technologies applicable for the manipulated objects are: A method for real-time tracking of the flexible objects during manipulation, a method for model-based offline prediction of the static deformation of grasped, flexible objects and, finally, a method for optimizing specific tasks based on both simulated and real-world executions.