Robot Behavior Research Articles

User Experience Design for Social Robots: A Case Study in Integrating Embodiment.

Social robotics is an emerging field with a high level of innovation. For many years, it was a concept framed in the literature and theoretical approaches. Scientific and technological advances have made it possible for robots to progressively make their way into different areas of our society, and now, they are ready to make the leap out of the industry and extend their presence into our daily lives. In this sense, user experience plays a fundamental role in achieving a smooth and natural interaction between robots and humans. This research focused on the user experience approach in terms of the embodiment of a robot, centring on its movements, gestures, and dialogues. The aim was to investigate how the interaction between robotic platforms and humans takes place and what differential aspects should be considered when designing the robot tasks. To achieve this objective, a qualitative and quantitative study was conducted based on a real interview between several human users and the robotic platform. The data were gathered by recording the session and having each user complete a form. The results showed that participants generally enjoyed interacting with the robot and found it engaging, which led to greater trust and satisfaction. However, delays and errors in the robot's responses caused frustration and disconnection. The study found that incorporating embodiment into the design of the robot improved the user experience, and the robot's personality and behaviour were significant factors. It was concluded that robotic platforms and their appearance, movements, and way of communicating have a decisive influence on the user's opinion and the way they interact.

Velocity Obstacle for Polytopic Collision Avoidance for Distributed Multi-Robot Systems

Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shaped robots is harder due to implicit and non-differentiable distance functions between polytopes. In this paper, we extend the concept of velocity obstacle (VO) principle for polytopic-shaped robots and propose a novel approach to construct the VO in the function of vertex coordinates and other robot's states. Compared with existing work about obstacle avoidance between polytopic-shaped robots, our approach is much more computationally efficient as the proposed approach for construction of VO between polytopes is optimization-free. Based on VO representation for polytopic shapes, we later propose a navigation approach for distributed multi-robot systems. We validate our proposed VO representation and navigation approach in multiple challenging scenarios including large-scale randomized tests, and our approach outperforms the state of art in many evaluation metrics, including completion rate, deadlock rate, and the average travel distance.

Walk-Burrow-Tug: Legged Anchoring Analysis Using RFT-Based Granular Limit Surfaces

We develop a new resistive force theory based granular limit surface (RFT-GLS) method to predict and guide behaviors of forceful ground robots. As a case study, we harness a small mobile robotic system - MiniRQuad (296g) - to ‘walk-burrow-tug;’ it actively exploits ground anchoring by burrowing its legs to tug loads. RFT-GLS informs the selection of efficient strategies to transport sleds with varying masses. The granular limit surface (GLS), a wrench boundary that separates stationary and kinetic behavior, is computed using 3D resistive force theory (RFT) for a given body and set of motion twists. This limit surface is then used to predict the quasi-static trajectory of the robot when it fails to withstand an external load. We find that the RFT-GLS enables accurate force and motion predictions in laboratory tests. For control applications, a pre-composed state space map of the twist-wrench pairs enables computationally efficient simulations to improve robotic anchoring strategies.

Adaptive assistive robotics: a framework for triadic collaboration between humans and robots.

Robots and other assistive technologies have a huge potential to help society in domains ranging from factory work to healthcare. However, safe and effective control of robotic agents in these environments is complex, especially when it involves close interactions and multiple actors. We propose an effective framework for optimizing the behaviour of robots and complementary assistive technologies in systems comprising a mix of human and technological agents with numerous high-level goals. The framework uses a combination of detailed biomechanical modelling and weighted multi-objective optimization to allow for the fine tuning of robot behaviours depending on the specification of the task at hand. We illustrate our framework via two case studies across assisted living and rehabilitation scenarios, and conduct simulations and experiments of triadic collaboration in practice. Our results indicate a marked benefit to the triadic approach, showing the potential to improve outcome measures for human agents in robot-assisted tasks.

Dynamic Analysis of a Delta Parallel Robot with Flexible Links and Joint Clearances

Delta robot is a lightweight parallel manipulator capable of accurately moving heavy loads at high speed and acceleration along a spatial trajectory. This intensive dynamic process may have a significant impact on the end-effector trajectory precision and motor behavior. The paper highlights the influence on the dynamic behavior of a Delta robot by considering individual and combined effects of clearances and friction in the spherical joints, as well as the flexibility of the rod elements. The CAD modeling of the Delta robot and its motion simulation on a representative spatial trajectory where the maximum allowed values of speed and acceleration are reached were performed using the Catia and Adams software packages. The obtained results show that the methods used were successfully applied and the effects are mutually interconnected, but not cumulative.

A biologically inspired decision-making system for the autonomous adaptive behavior of social robots

The decisions made by social robots while they fulfill their tasks have a strong influence on their performance. In these contexts, autonomous social robots must exhibit adaptive and social-based behavior to make appropriate decisions and operate correctly in complex and dynamic scenarios. This paper presents a Decision-Making System for social robots working on long-term interactions like cognitive stimulation or entertainment. The Decision-making System employs the robot’s sensors, user information, and a biologically inspired module to replicate how human behavior emerges in the robot. Besides, the system personalizes the interaction to maintain the users’ engagement while adapting to their features and preferences, overcoming possible interaction limitations. The system evaluation was in terms of usability, performance metrics, and user perceptions. We used the Mini social robot as the device where we integrated the architecture and carried out the experimentation. The usability evaluation consisted of 30 participants interacting with the autonomous robot in 30 min sessions. Then, 19 participants evaluated their perceptions of robot attributes of the Godspeed questionnaire by playing with the robot in 30 min sessions. The participants rated the Decision-making System with excellent usability (81.08 out of 100 points), perceiving the robot as intelligent (4.28 out of 5), animated (4.07 out of 5), and likable (4.16 out of 5). However, they also rated Mini as unsafe (security perceived as 3.15 out of 5), probably because users could not influence the robot’s decisions.

Simulation Power vs. Immersive Capabilities: Enhanced Understanding and Interaction with Digital Twin of a Mechatronic System

Automation Studio, a specialised simulation software, offers virtual commissioning capabilities and robust tools for modelling the behaviour and performance of a pneumatic robot controlled by a PLC. Conversely, Unity is a versatile platform primarily used for creating high-quality 3D games and interactive simulations, providing immersive experiences with DTs through mixed-reality environments. This paper provides a study that compares and contrasts the simulation power of Automation Studio and the immersive capabilities of Unity in the context of developing digital twins for a mechatronic system. This research explores how these complementary approaches enhance the development, validation, understanding, and interaction with digital twins. By examining both platforms in this context, the article provides valuable insights for engineers, developers, and researchers looking to create digital twins for mechatronic systems, but not only. This study demonstrates the potential of leveraging the combined power of simulation and immersive capabilities to improve the interaction between the real robotic arm manipulator cylindrical type and its digital twin in different scenarios, using an OPC approach for mirroring. The combination of Automation Studio and Unity provides a powerful platform for applied science education in the field of the digital twin of mechatronic systems.

Assistive technology implementation with brain-machine interface

As the development of the Brain Computer Interface, it can be a possible way for human to control the external devices by the mind. This can be good news for the handicapped people whose life qualities are greatly decreased. In this paper, designed an assistive robot arm for those disabled. The assistive robot arm mainly contains a filter, a microcontroller and a motor drive system. The filter can filter out the noises while the microcontroller judging the brain signals and turn it into an actual control signal. The actual control signal will finally drive the motor and for the sake of safety and stability, a PID circuit was add in the motor drive system. The mechanic arm designed can turn what people think to the actual robot behaviour and this will obviously increase the life quality of the disabled people. Its also a light weight system with a wide range of versatility and will bring benefits to all the handicapped.

INVESTIGATION OF DYNAMICS OF A PIPE ROBOT WITH SELF-STOPPING MECHANISM

Pipe robots are used in agricultural engineering for transportation of various materials inside the pipe as well as for cleaning of internal surfaces of the pipes.
 In this paper a model of a pipe robot with self-stopping mechanism is proposed and described in detail. Numerical investigations are performed, and the obtained results are presented in the form of graphical relationships. Variations of displacements as well as variations of velocities of the exciting mass and of the case of the pipe robot as functions of time are investigated. Results for steady state regime of motion are presented.
 Displacement of the first degree of freedom, velocity of the first degree of freedom, displacement of the second degree of freedom, velocity of the second degree of freedom as functions of time in the steady state regime of motion are investigated. Also, difference of displacements as well as difference of velocities as functions of time in the steady state regime of motion are represented.
 Results for three typical values of the constant force are described in the paper: first the results when the constant force is assumed equal to zero are presented and then the results for the two typical nonzero values of the constant force are presented. From the obtained graphical representations, the influence of the value of the constant force to the dynamic behavior of the pipe robot can be seen.
 The obtained graphical representations enable to understand the behavior of the investigated pipe robot with self-stopping mechanism.
 Keywords: pipe robot, self-stopping mechanism, harmonic excitation, steady state motions, parameters of the system.

Non-Commutative Logic for Collective Decision-Making with Perception Bias

In this paper, we suggest an implementation of non-commutative logic and apply its operators for decision-making in a group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of non-commutativity”. The value of this parameter is specified using the perception bias of humans measured in the experiments. The suggested operators and decision-making method are illustrated by the simulated behavior of mobile robots in the group, which verified the possibility of processing systematic sensing errors, as well as of distinguishing and mimicking the biased decisions.

A motivational model based on artificial biological functions for the intelligent decision-making of social robots

Modelling the biology behind animal behaviour has attracted great interest in recent years. Nevertheless, neuroscience and artificial intelligence face the challenge of representing and emulating animal behaviour in robots. Consequently, this paper presents a biologically inspired motivational model to control the biological functions of autonomous robots that interact with and emulate human behaviour. The model is intended to produce fully autonomous, natural, and behaviour that can adapt to both familiar and unexpected situations in human–robot interactions. The primary contribution of this paper is to present novel methods for modelling the robot’s internal state to generate deliberative and reactive behaviour, how it perceives and evaluates the stimuli from the environment, and the role of emotional responses. Our architecture emulates essential animal biological functions such as neuroendocrine responses, circadian and ultradian rhythms, motivation, and affection, to generate biologically inspired behaviour in social robots. Neuroendocrinal substances control biological functions such as sleep, wakefulness, and emotion. Deficits in these processes regulate the robot’s motivational and affective states, significantly influencing the robot’s decision-making and, therefore, its behaviour. We evaluated the model by observing the long-term behaviour of the social robot Mini while interacting with people. The experiment assessed how the robot’s behaviour varied and evolved depending on its internal variables and external situations, adapting to different conditions. The outcomes show that an autonomous robot with appropriate decision-making can cope with its internal deficits and unexpected situations, controlling its sleep–wake cycle, social behaviour, affective states, and stress, when acting in human–robot interactions.

Designing Behaviors of Robots Based on the Artificial Emotion Expression Method in Human–Robot Interactions

How to express emotions through motion behaviors of robots (mainly for robotic arms) to achieve human–robot emotion interactions is the focus of this paper. An artificial emotion expression method that accords with human emotion that can deal with external stimuli and has the capability of emotion decision-making was proposed based on the motion behaviors of robot. Firstly, a three-dimensional emotion space was established based on the motion indexes (deviation coefficient, acceleration, and interval time). Then, an artificial emotion model, which was divided into three parts (the detection and processing of external events, the generation and modification of emotion response vectors, and the discretization of emotions) was established in the three-dimensional emotion space. Then emotion patterns (love, excited, happy, anxiety, hate) and emotion intensity were calculated based on the artificial emotion model in human–robot interaction experiments. Finally, the influence of motion behaviors of humanoid robot NAO on the emotion expression of experimenters was studied through human–robot emotion interaction experiments based on the emotion patterns and emotion intensity. The positive emotion patterns (love, excited, happy) and negative emotion patterns (anxiety, hate) of the experimenters were evaluated. The experimental results showed that the personalized emotion responses could be generated autonomously for external stimuli, and the change process of human emotions could be simulated effectively according to the established artificial emotion model. Furthermore, the experimenters could recognize the emotion patterns expressed by the robot according to the motion behaviors of the robot, and whether experimenters were familiar with robots did not influence the recognition of different emotion patterns.

High‐performance graphics processing unit‐based strategy for tuning a unmanned aerial vehicle controller subject to time‐delay constraints

AbstractRecently, high‐performance computing strategies have been implemented to improve performance analysis and reduce the development time of new solutions in robotic applications, such as path planning, machine learning, and vision, which require massive matrix computations. In this sense, this work aims to study the aerial robots' behavior during their mission execution. Due to the large search space in the set of parameter combinations and the high computational cost required to perform such an analysis after sequentially executing thousands of simulations, this work proposes an open‐source graphics processing unit (GPU)‐based implementation to simulate the robot behavior. A GPU‐accelerated flight route analysis for multi‐unmanned aerial vehicle (UAV) systems is proposed for the tuning control problem in the parameters‘ space considering the problem of delay in sending information to a ground control station. Considering our implementation, the experimental results show a speedup up to 325, 629, and 5959 in comparison to the parallel version with 16 threads, C coder converter, and native Matlab code, respectively. The implementation is available in the Colab Google platform and it can easily be expanded for analyses involving larger amounts of different parameters, robot models, strategies, and controllers.

Signal Communication for Collaborative Robot Behavior Control

Robots are increasingly required to work in close cooperation with humans and other robots, performing common tasks jointly, in collaboration. High-level decision making by an autonomous agent with such interactions require specific means of responding to the situation. This paper describes the implementation of the control system for a collaborative robot, based on the emotion-need architecture that provides reliable and interpretable interaction means of the robot with other agents. The robot is an autonomous device whose behavior is aimed at satisfying its current needs. The communication between the operator and the robot is based on signal communication: the operator’s signal ("command") activates an element of the emotion-need scheme — a gateway that implements the emotional feedback. Since the gateway connects the sensors and behavioral procedures, its activation starts the corresponding procedure, as if the sensory system had registered a real stimulus. The external signal of such indirect control can be represented in acoustic form or as the operator’s posture. The needs of the robot are aimed at both its physical survival (danger avoidance) and maintenance of the social connections (accompanying friendly agents). The robot recognizes external commands and activates the gateway associated with the highest priority action. The activity also gradually fades, which allows actions to be performed for some time even in the absence of an appropriate internal stimulus. The resulting robot is able to perform the simplest patrol tasks together with a human, acting in a predictable and understandable (interpretable) way for the human.

Perceptions of intelligence & sentience shape children’s interactions with robot reading companions

The potential for robots to support education is being increasingly studied and rapidly realised. However, most research evaluating education robots has neglected to examine the fundamental features that make them more or less effective, given the needs and expectations of learners. This study explored how children’s perceptions, expectations and experiences are shaped by aesthetic and functional features during interactions with different robot ‘reading buddies’. We collected a range of quantitative and qualitative measures of subjective experience before and after children read a book with one of three different robots. An inductive thematic analysis revealed that robots have the potential offer children an engaging and non-judgemental social context to promote reading engagement. This was supported by children’s perceptions of robots as being intelligent enough to read, listen and comprehend the story, particularly when they had the capacity to talk. A key challenge in the use of robots for this purpose was the unpredictable nature of robot behaviour, which remains difficult to perfectly control and time using either human operators or autonomous algorithms. Consequently, some children found the robots’ responses distracting. We provide recommendations for future research seeking to position seemingly sentient and intelligent robots as an assistive tool within and beyond education settings.

Effect of Different Listening Behaviors of Social Robots on Perceived Trust in Human-robot Interactions

With the increased use of social robots in prominence and beyond functional performance, they are expected to foster trust and confidence in people. Various factors involve providing social robots with more trustworthy behavior. This study investigated whether the listening behavior of a social robot can affect the perception of being trustworthy in human–robot interaction. Therefore, we designed four different listening behaviors, including nonactive listening, active listening, active empathic listening, and verbal-only empathic listening, for a social robot and evaluated the impact of each behavior on the participants’ likelihood of trusting the robot, using a between-subject design. Participants in the four conditions conversed with a robot that simulated one of the listening behaviors, and their general, cognitive and affective trust toward the robot was measured. The results indicated that active empathic listening behavior provided the participants with the highest impression of trustworthiness, specifically in affective trust. Both active listening and active empathic listening were evaluated higher than nonactive listening in general, affective, and cognitive trust. However, active empathic listening behavior was differentiated from active listening behavior only in terms of affective trust. For verbal and nonverbal dimensions of listening behaviors, it was confirmed that nonverbal behaviors such as nodding, body movement, and eye gaze along with verbal behaviors, had a significant effect in eliciting higher affective trust in human-robot interaction. Consequently, we concluded that designing social robots with active (empathic) listening behavior can enhance trust perception in human-robot interaction in different fields such as education, healthcare, and business.

Development of robust sensor packages for autonomous underwater vehicles

New generations of robots are designed to support humans with a variety of partially or fully automated services. Such flexible and mobile service robots cooperate with humans or even act completely independent. To achieve this, it is necessary to significantly improve their capabilities in terms of environment perception, data processing and movement. At the same time, they must meet the highest standards of reliability and safety. Innovative electronics enable the necessary improvements and thus appropriate robot behavior. The aim of the Bionic RoboSkin project is to enhance the possibilities of a robot platform that is capable of autonomously navigating its respective environment by means of a flexible bionic sensor skin. The sensor platform is an autonomous underwater vehicle [AUV] that is based on the bionic principles of a Manta Ray [1, 2, 3]. The newly developed sensor skin consists of a textile composite as a carrier for sensor elements and provides moisture-resistant electrical connections for energy supply and communication. The integrated sensor modules enable both the detection of touch and approach and the exploration of the environment. The functionality of the sensor skin is targeting two service robotics applications: autonomous surveying of underwater structures (e.g. inspection of pipelines) and semi-autonomous geo-exploration in difficult-to-access areas (e.g. monitoring in tunnel construction). As a result, the paper presents the concept of a modular packaging platform for the use in a harsh marine environment. The technologies used for the miniaturization of the sensor module by PCB embedding and for outer housing development will be discussed in detail and a strong focus is put on packaging material properties in sub-marine conditions.

Codifying Wildness: Wild Behaviour for Improving Human-Robot Interaction

Animals, even when domesticated, maintain a series of innate characteristics that humans perceive as wild. Human responses to animal behavior vary with many factors such as historical time and culture. Even after their domestication, humans perceive animals’ distinctive behavior patterns that are considered as being wild, even in pets. It is common understanding that these wild patterns are linked to the specific animal species but also that there is a common characteristic to wild behavior in general. In this paper, we present our work on coding such a quality, exemplified in an application to robot pets. We consider that an application of wild character in the behavior of the pet robot can improve its realism and make the human/robot social interaction more captivating. To this aim, we have designed and implemented a behavioral architecture to reproduce well-known behavioral patterns of domestic cats that might be considered wild and applied it to a commercial pet robot that then was used to assess human reaction to its behavior. We demonstrate the effectiveness of the proposed approach with experiments carried out with humans that found the robot cat “independent” and “unpredictable” to a significant degree, two distinctive features of the wild side of animals.

Digital Twin (DT)-CycleGAN: Enabling Zero-Shot Sim-to-Real Transfer of Visual Grasping Models

Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of “reality gap”. In response, this paper presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged. Our core innovation is that real and virtual DT robots are forced to mimic each other in a way that the gaps or differences between simulated and realistic robotic behaviors are minimized. To effectively realize this innovation, visual grasping is employed as an exemplar robotic task, and the reality gap in zero-shot Sim-to-Real transfer of visual grasping models is defined as grasping action consistency losses and intrinsically penalized during the DT-CycleGAN training process in realistic simulation environments. Specifically, first, cycle consistency losses between real visual images and simulation images are defined and minimized to reduce the reality gaps in visual appearances during visual grasping tasks. Second, the grasping agent's action consistency losses are defined and penalized to minimize the inconsistency of the grasping agent's actions between the virtual states generated by the DT-CycleGAN generator and the real visual states. By jointly penalizing both the cycle consistency losses and the grasping agent's action consistency losses in DT-CycleGAN, the reality gaps in both visual appearance and grasping action states in simulated and real environments are minimized, thus significantly contributing to the effective and robust zero-shot Sim-to-Real transfer of the trained visual grasping models. Extensive experiments demonstrated the effectiveness and efficiency of our novel DT-CycleGAN framework for zero-shot Sim-to-Real transfer. Our code and models are released on GitHub to facilitate other researchers' works in this promising direction <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula> .

Face to Face with a Sexist Robot: Investigating How Women React to Sexist Robot Behaviors

Face to Face with a Sexist Robot: Investigating How Women React to Sexist Robot Behaviors