Robot Behavior Research Articles

Sociable Robot ‘Lometh’: Exploring Interactive Regions of a Product-Promoting Robot in a Supermarket

The robot ‘Lometh’ is an information-presenting robot that naturally interacts with people in a supermarket environment. In recent years, considerable effort has been devoted to the implementation of robotic interfaces to identify effective behaviors of communication robots focusing only on the social and physical factors of the addresser and the hearer. As attention focus and attention target shifting of people differs based on the human visual focus and the spatiality, this study considered four interactive regions, considering the visual focus of attention as well as the interpersonal space between robot and human. The collected primary data revealed that 56% attention shifts occurred in near peripheral field of view regions and 44% attention shifts in far peripheral field of view regions. Using correspondence analysis, we identified that the bodily behaviors of the robot showed the highest success rate in the left near peripheral field of view region. The verbal behaviors of the robot captured human attention best in the right near peripheral field of view region. In this experiment of finding a socially acceptable way to accomplish the attention attracting goals of a communication robot, we observed that the robots’ affective behaviors were successful in shifting human attention towards itself in both left and right far- peripheral field of view regions, so we concluded that for far field of view regions, designing similar interaction interventions can be expected to be successful.

Can industrial robot applications help cross the middle-income trap? — Empirical evidence based on crossed-country panel data

What middle-income countries can draw from the experience of the countries that have successfully crossed the middle-income trap has become a theoretical and practical issue where an in-depth study is demanded as these countries account for a large proportion of the world's economy. There are many research studies on crossing the middle-income trap and artificial intelligence, but few studies have combined the two. This paper investigates the impact of industrial robot applications on the middle-income trap crossing at both theoretical and empirical levels. In theory, the scale effect and pricing behavior of robots are introduced into the task model proposed by Acemoglu and Restrepo (2018a) in this paper, which proves that robots can affect economic growth directly, and the greater the robot application, the greater the total output. In terms of empirical analysis, this paper draws on the classification criteria of Felipe et al. (2014) and adopts the absolute value method to classify economies into five groups: trapped, crossed, high-income, middle-income, and low-income group. Based on the panel data of 24 trapped and 37 leapfrogged economies from 1993 to 2019, we have empirically examined the effect of industrial robot application on crossing the middle-income trap. The results indicate that industrial robot applications facilitate economies to cross the middle-income trap through technological innovation, industrial structure and FDI, of which industrial structure includes both output structure and employment structure. This paper provides empirical facts and policy implications for developing economies to cross the middle-income trap through industrial applications.

Core Challenges of Social Robot Navigation: A Survey

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in crowded environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in robot planning, behavior design, and evaluation methodologies. Within these categories, we review past work and offer directions for future research. Our work builds upon and extends earlier survey efforts by (a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and (b) offering constructive feedback and ideas that could inspire research in the field over the coming decade.

Integrating Virtual Reality and Robotic Operation System (ROS) for AGV Navigation

The use of AGVs (Automated Guided Vehicles) is rapidly expanding in various applications and industries, meeting the growing demand for automated material handling systems. However, AGV control and navigation remain a challenge. To address this issue, robotics simulators such as ROS (Robot Operating System) have become widely used, reducing the cost and time of checking robot performance. Furthermore, the integration of virtual reality technology into the robotics field has facilitated the study of various robot behaviors in realistic environments, replicating the robot’s real-life size and dimensions. In this study, the TurtleBot2i and RAZBOT AGV robot platforms were integrated into the 3D Unity environment and controlled using ROS. Using Unity as a simulator for the robot’s working environment offers several benefits, including high-quality graphics and a detailed examination of the robot’s behavior. The results of the study demonstrate the accurate simulation and control of the AGV platforms in both ROS and Unity environments.

ECR Spotlight – Talia Moore

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology during our centenary year but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Talia Moore is an author on ‘ Jumping over fences: why field- and laboratory-based biomechanical studies can and should learn from each other’, published in JEB. Talia is an Assistant Professor at the University of Michigan, USA, investigating bio-inspired robotic design, and biomechanics and behavior of unsteady terrestrial motion.

Kinetostatic Modeling of Soft Robots: Energy-Minimization Approach and 99-Line MATLAB Implementation.

Soft robots have received a great deal of attention from both academia and industry due to their unprecedented adaptability in unstructured environment and extreme dexterity for complicated operations. Due to the strong coupling between the material nonlinearity due to hyperelasticity and the geometric nonlinearity due to large deflections, modeling of soft robots is highly dependent on commercial finite element software packages. An approach that is accurate and fast, and whose implementation is open to designers, is in great need. Considering that the constitutive relation of the hyperelastic materials is commonly expressed by its energy density function, we present an energy-based kinetostatic modeling approach in which the deflection of a soft robot is formulated as a minimization problem of its total potential energy. A fixed Hessian matrix of strain energy is proposed and adopted in the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, which significantly improves its efficiency for solving the minimization problem of soft robots without sacrificing prediction accuracy. The simplicity of the approach leads to an implementation of MATLAB with only 99-line codes, which provides an easy-to-use tool for designers who are designing and optimizing the structures of soft robots. The efficiency of the proposed approach for predicting kinetostatic behaviors of soft robots is demonstrated by seven pneumatic-driven and cable-driven soft robots. The capability of the approach for capturing buckling behaviors in soft robots is also demonstrated. The energy-minimization approach, as well as the MATLAB implementation, could be easily tailored to fulfill various tasks, including design, optimization, and control of soft robots.

Prediction of behavior of a noncommunicative robot based on observations of its movement

AbstractAn adversary robot can move around in a free space “patrolling an area” appearing to have a random “behavior” if observed through a “lens.” The “behavior” of the robot is associated with its observed/internal state. Stochastic techniques or machine learning techniques, which can be used to predict the future states of an adversary robot demand high computation and thus, power. Advanced RNN techniques like long short‐term memory, which can make predictions using long‐term dependencies of the sequence data, also have extremely high computing and memory requirements. Thus, the systems involved in using these former techniques are not appropriate to be deployed in fields which require inconspicuous monitoring. In this work, we present two novel methods using the “behavior” to predict the future states of an adversary robot which can be deployed using a low computing device/small single board computer/robot in a non‐GPS environment without any internet connection. The proposed two methods are based on a list and a graph, respectively. Unlike the most available literature, where the prediction is based on “time,” our model predicts the future state using the states of the system. These methods work for both single and multiple‐looped paths taken by the adversary robot. The results show that the prediction error which depends on the predicted move and the actual move taken by the robot decreases as a logarithmic function and is comparatively lower when the adversary robot follows a path having multiple loops.

From Inanimate Object to Agent: Impact of Pre-beginnings on the Emergence of Greetings with a Robot

The very first moments of co-presence, during which a robot appears to a participant for the first time, are often “off-the-record” in the data collected from human-robot experiments (video recordings, motion tracking, methodology sections, etc.). Yet, this “pre-beginning” phase, well documented in the case of human-human interactions, is not an interactional vacuum: It is where interactional work from participants can take place so the production of a first speaking turn (like greeting the robot) becomes relevant and expected. We base our analysis on an experiment that replicated the interaction opening delays sometimes observed in laboratory or “in-the-wild” human-robot interaction studies—where robots can require time before springing to life after they are in co-presence with a human. Using an ethnomethodological and multimodal conversation analytic methodology (EMCA), we identify which properties of the robot's behavior were oriented to by participants as creating the adequate conditions to produce a first greeting. Our findings highlight the importance of the state in which the robot originally appears to participants: as an immobile object or, instead, as an entity already involved in preexisting activity. Participants’ orientations to the very first behaviors manifested by the robot during this “pre-beginning” phase produced a priori unpredictable sequential trajectories, which configured the timing and the manner in which the robot emerged as a social agent. We suggest that these first instants of co-presence are not peripheral issues with respect to human-robot experiments but should be thought about and designed as an integral part of those.

Speed Control of Wheeled Mobile Robot by Nature-Inspired Social Spider Algorithm-Based PID Controller

Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties.

Robotic gaze and human views: A systematic exploration of robotic gaze aversion and its effects on human behaviors and attitudes.

Similar to human-human interaction (HHI), gaze is an important modality in conversational human-robot interaction (HRI) settings. Previously, human-inspired gaze parameters have been used to implement gaze behavior for humanoid robots in conversational settings and improve user experience (UX). Other robotic gaze implementations disregard social aspects of gaze behavior and pursue a technical goal (e.g., face tracking). However, it is unclear how deviating from human-inspired gaze parameters affects the UX. In this study, we use eye-tracking, interaction duration, and self-reported attitudinal measures to study the impact of non-human inspired gaze timings on the UX of the participants in a conversational setting. We show the results for systematically varying the gaze aversion ratio (GAR) of a humanoid robot over a broad parameter range from almost always gazing at the human conversation partner to almost always averting the gaze. The main results reveal that on a behavioral level, a low GAR leads to shorter interaction durations and that human participants change their GAR to mimic the robot. However, they do not copy the robotic gaze behavior strictly. Additionally, in the lowest gaze aversion setting, participants do not gaze back as much as expected, which indicates a user aversion to the robot gaze behavior. However, participants do not report different attitudes toward the robot for different GARs during the interaction. In summary, the urge of humans in conversational settings with a humanoid robot to adapt to the perceived GAR is stronger than the urge of intimacy regulation through gaze aversion, and a high mutual gaze is not always a signof high comfort, as suggested earlier. This result can be used as a justification to deviate from human-inspired gaze parameters when necessary for specific robot behavior implementations.

Liquid Metal Smart Materials toward Soft Robotics

Unlike conventional rigid machines, soft robots generally have unique operation styles that rely heavily on soft matter engineering and smart material systems. Owing to the superior merits of both metals and fluids, liquid metal smart materials are increasingly being innovatively used as soft actuators and sensors to construct soft robots. To promote further development and prosperity in this field, this review organizes and summarizes the typical progress in liquid metal smart materials, with a special focus on their robotic response behaviors. In particular, the article emphasizes the concept of smart composite systems consisting of liquid metals and synergistic substances (e.g., solutions, particles, and polymers). The response behaviors of liquid metal smart materials under the actions of external factors (electricity, magnetism, ultrasound, light, heat, etc.) are examined and the smart properties occurring during these responses, such as motion, transformation, self‐organization, adaptive healing, and autonomous sensing, are identified. Furthermore, soft actuators, control systems, and robots based on liquid metal smart materials are summarized and elaborated upon. Finally, the potential directions worth pursuing and challenges are outlined. It is expected that this review will stimulate further investigations into liquid metal smart materials with the aim of building a new generation of soft robots.

Factors Impacting Diversity and Effectiveness of Evolved Modular Robots

In many natural environments, different forms of living organisms successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in artificial agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both morphology and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, morphology and controller of VSRs for the task of locomotion. We investigate experimentally whether three key factors—representation, Evolutionary Algorithm (EA), and environment—impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise an automatic machine learning pipeline for systematically characterizing the morphology and behavior of robots resulting from the optimization process. We classify the robots into species and then measure biodiversity in populations of robots evolved in a multitude of conditions resulting from the combination of different morphology representations, controller representations, EAs, and environments. The experimental results suggest that, in general, EA and environment matter more than representation. We also propose a novel EA based on a speciation mechanism that operates on morphology and behavior descriptors and we show that it allows to jointly evolve morphology and controller of effective and diverse VSRs.

Effects of task structure and attribution target on responsibility attributions in human–robot interaction

Responsibility attribution refers to beliefs about the cause of an event or outcome. This study investigates the effects of cooperation and competition on responsibility attributions after success and failure in human–robot interaction. A 2 × 2 × 2 mixed-design experiment was conducted, with task structure (cooperation vs. competition) and attribution target (self vs. robot) as within-subject variables and task outcome (win vs. lose) as a between-subject variable. Forty students from a university campus participated in the experiment. They cooperated with or competed against a humanoid robot in a picture memory game whose outcome either exceeded (success) or did not reach (failure) an expected outcome. Afterward, participants allocated the causal responsibility between themselves and the humanoid robot for a specified performance success or failure. It was found that participants attributed more responsibility for success to themselves than to the humanoid robot, and such self-serving bias was greater in competitive than in cooperative structures. Additionally, participants attributed more responsibility for failure to themselves than to the humanoid robot in cooperative structures but not in competitive structures. Moreover, the competitiveness index of unsuccessful participants moderated the relationship between the task structure and the differential responsibility attribution between the self and the robot. Furthermore, participants evaluated the humanoid robot more positively in cooperative than competitive structures. Finally, participants had higher accuracy in cooperative structures, although they subjectively perceived that they performed better in competitive structures. These findings provide insight to guide people's attributional behavior, task performance, and evaluation of robots in the desired manner from the perspective of task structure design.

Mind Perception in HRI: Exploring Users’ Attribution of Mental and Emotional States to Robots with Different Behavioural Styles

Theory of Mind is crucial to understand and predict others’ behaviour, underpinning the ability to engage in complex social interactions. Many studies have evaluated a robot’s ability to attribute thoughts, beliefs, and emotions to humans during social interactions, but few studies have investigated human attribution to robots with such capabilities. This study contributes to this direction by evaluating how the cognitive and emotional capabilities attributed to the robot by humans may be influenced by some behavioural characteristics of robots during the interaction. For this reason, we used the Dimensions of Mind Perception questionnaire to measure participants’ perceptions of different robot behaviour styles, namely Friendly, Neutral, and Authoritarian, which we designed and validated in our previous works. The results obtained confirmed our hypotheses because people judged the robot’s mental capabilities differently depending on the interaction style. Particularly, the Friendly is considered more capable of experiencing positive emotions such as Pleasure, Desire, Consciousness, and Joy; conversely, the Authoritarian is considered more capable of experiencing negative emotions such as Fear, Pain, and Rage than the Friendly. Moreover, they confirmed that interaction styles differently impacted the perception of the participants on the Agency dimension, Communication, and Thought.

A Hierarchical Design Framework for the Design of Soft Robots

This paper demonstrates the effectiveness of a hierarchical design framework in developing environment-specific behaviour for fluid-actuated soft robots. Our proposed framework employs multi-step optimisation and reduced-order modelling to reduce the computational expense associated with simulating non-linear materials used in the design process. Specifically, our framework requires the designer to make high-level decisions to simplify the optimisations, targeting simple objectives in earlier steps and more complex objectives in later steps. We present a case study, where our proposed framework is compared to a conventional direct design approach for a simple 2D design. A soft pneumatic bending actuator was designed that is able to perform asymmetrical motion when actuated cyclically. Our results show that the hierarchical framework can find almost 2.5 times better solutions in less than 3% of the time when compared to a direct design approach.

The Relationships of Human-Cobot Interaction Fluency with Job Performance and Job Satisfaction among Cobot Operators—The Moderating Role of Workload

Modern factories are subject to rapid technological changes, including the advancement of robotics. A key manufacturing solution in the fourth industrial revolution is the introduction of collaborative robots (cobots), which cooperate directly with human operators while executing shared tasks. Although collaborative robotics has tangible benefits, cobots pose several challenges to human-robot interaction. Proximity, unpredictable robot behavior, and switching the operator's role from a co-operant to a supervisor can negatively affect the operator's cognitive, emotional, and behavioral responses, resulting in their lower well-being and decreased job performance. Therefore, proper actions are necessary to improve the interaction between the robot and its human counterpart. Specifically, exploring the concept of human-robot interaction (HRI) fluency shows promising perspectives. However, research on conditions affecting the relationships between HRI fluency and its outcomes is still in its infancy. Therefore, the aim of this cross-sectional survey study was twofold. First, the relationships of HRI fluency with job performance (i.e., task performance, organizational citizenship behavior, and creative performance) and job satisfaction were investigated. Second, the moderating role of the quantitative workload in these associations was verified. The analyses carried out on data from 200 male and female cobot operators working on the shop floor showed positive relationships between HRI fluency, job performance, and job satisfaction. Moreover, the study confirmed the moderating role of the quantitative workload in these relations. The results showed that the higher the workload, the lower the relationships between HRI fluency and its outcomes. The study findings are discussed within the theoretical framework of the Job Demands-Control-Support model.

Say what you want, I'm not listening!: A conversational self-reflection robot that does not parse user speech.

We present a conversational social robot behaviour design that draws from psychotherapy research to support individual self-reflection and wellbeing, without requiring the robot to parse or otherwise understand what the user is saying. This simplicity focused approached enabled us to intersect the well-being aims with privacy and simplicity, while achieving high robustness. We implemented a fully autonomous and standalone (not network enabled) prototype and conducted a proof-of-concept study as an initial step to test the feasibility of our behaviour design: whether people would successfully engage with our simple behaviour and could interact meaningfully with it. We deployed our robot unsupervised for 48h into the homes of 14 participants. All participants engaged with self-reflection with the robot without reporting any interaction challenges or technical issues. This supports the feasibility of our specific behaviour design, as well as the general viability of our non-parsing simplicity approach to conversation, which we believe to be an exciting avenue for further exploration. Our results thus pave the way for further exploring how conversational behaviour designs like ours may support people living with loneliness.

Priority-based state machine synthesis that relaxes behavior design of multi-arm manipulators in dynamic environments

This paper presents a framework governing multiple state machines (SMs) to generate behaviors for kinematically-coupled multi-arm robots. A SM is one of the most used tools to switch controllers in robotics, however, it represents only one motion at a certain time. Such a limit of representation not only restricts the potential of the kinematical redundancy but also significantly increases the number of situations we have to consider in behavior design. To relax the problem, this paper provides a distributed design scheme and an online synthesis method for SMs. The base of the framework is a task-priority-based control method for redundant robots. We expand it by introducing an abstract data structure specialized in robotic behaviors. The abstract reformulation naturally connects with SMs so that the framework realizes parallelism of sequential logic. A priority queue constructed in each control loop integrates SMs designed for each effector, and the framework automatically generates whole-body behaviors online. In dynamic simulations, the framework governs four SMs and achieves reaching tasks with a dual-arm manipulator at 1 kHz. It is shown that the loose coupling of the SMs yields reactive and flexible decisions in a dynamic environment.

Perspective on investigation of neurodegenerative diseases with neurorobotics approaches

Neurorobotics has emerged from the alliance between neuroscience and robotics. It pursues the investigation of reproducing living organism-like behaviors in robots by means of the embodiment of computational models of the central nervous system. This perspective article discusses the current trend of implementing tools for the pressing challenge of early-diagnosis of neurodegenerative diseases and how neurorobotics approaches can help. Recently, advances in this field have allowed the testing of some neuroscientific hypotheses related to brain diseases, but the lack of biological plausibility of developed brain models and musculoskeletal systems has limited the understanding of the underlying brain mechanisms that lead to deficits in motor and cognitive tasks. Key aspects and methods to enhance the reproducibility of natural behaviors observed in healthy and impaired brains are proposed in this perspective. In the long term, the goal is to move beyond finding therapies and look into how researchers can use neurorobotics to reduce testing on humans as well as find root causes for disease.

Asymmetric Continuum Robots

Continuum robots are flexible mechanisms that enable tasks in cluttered environments where conventional robots cannot operate. However, their few design parameters and task/manufacturing constraints leave small margins for optimization. In this paper, we expand the design space of continuum robots by introducing asymmetries. Asymmetric robot kinematics show how the new design parameters affect robot behavior and provide a framework for task-oriented design. The theoretical results are validated on a prototype with an automatically-generated asymmetric design, which navigates through an environment inaccessible to equivalent symmetric robots.