Keywords: Intelligent road transportation, Smart cities
Abstract: Boundedly Rational User Equilibria (BRUE) capture situations where all agents on a transportation network are electing the fastest option up to some time indifference, and serve as a relaxation of User Equilibria (UE), where each agent exactly minimizes their travel time. We study how the social cost under BRUE departs from that of UE in the context of static demand and stochastic costs, along with the implications of BRUE on the optimal signaling scheme of a benevolent central planner. We show that the average excess time is sublinear in the maximum time indifference of the agents, though such aggregate may hide disparity between populations and the sublinearity constant depends on the topology of the network. Regarding the design of public signals, even though in the limit where agents are totally indifferent, it is optimal to not reveal any information, there is in general no trend in how much information is optimally disclosed to agents. What is more, an increase in information disclosed may either harm or benefit agents as a whole.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems, Public policies, Semi-autonomous and mixed-initiative systems
Abstract: Incentive design problems entail asymmetry in the decision-making process in multi-agent systems, where in simplest terms one agent (inducer} attempts to control the decision outcome of another agent (inducee) to his (inducer’s) favor (though variations exist where the numbers of inducers and inducees could be more than one). The basic framework allows the inducer and the inducee to share a common (noisy) information on the state of the environment, but in addition for the inducer to have access to the decision (action) of the inducee and possibly also additional private information on the state. Most incentive design literature heretofore has addressed the problem where all random variables are defined on a common probability space, which is common knowledge to all agents. In this paper, we relax this basic assumption that the agents work with the same probability model, and endow them instead with disparate probability measures, such as for example them having different perceptions (beliefs) on second moments of the underlying random variables. We investigate the extent to which such “probabilistic multi-modeling” affects the design of effective incentive policies, both when this disparity is common knowledge to the agents and when it is not. Finally, we present results for the extended framework when there are multiple inducees with multiple probabilistic outlooks, with the solution concept capturing their interaction being Nash equilibrium.

Keywords: Smart infrastructure, Intelligent road transportation
Abstract: We study adaptive pricing for a routing game to learn unknown value of time and unknown coefficients of link latency functions, which are collectively referred to as the parameters of the game. The input for each trial is pricing for each link and the output is the resulting Nash flow on the links. For affine link latency functions, we provide sufficient conditions on unique identifiability of the parameters. We also propose a Laplacian adaptive pricing, which asymptotically leads to equal usage of all the links, and which when combined with a randomization schedule generates samples that satisfy the sufficient condition in finite trials with high probability. We performed human subject traffic assignment experiments, and find that the estimated value of time is lower than the one estimated by a known adaptive stated preference method, but is observed to be quite effective in inducing social equilibrium.

Keywords: Smart cities, Smart infrastructure, Urban mobility
Abstract: We consider a Stackelberg game in which a principal (she) establishes a two-stage contract with a non-myopic agent (he) whose type is unknown. The contract takes the form of an incentive function mapping the agent's first-stage action to his second-stage incentive. While the first-stage action reveals the agent's type under truthful play, a non-myopic agent could benefit from portraying a false type in the first stage to obtain a larger incentive in the second stage. The challenge is thus for the principal to design the incentive function so as to induce truthful play. We show that this is only possible with a constant, non-reactive incentive functions when the type space is continuous, whereas it can be achieved with reactive functions for discrete types. Additionally, we show that introducing an adjustment mechanism that penalizes inconsistent behavior across both stages allows the principal to design more flexible incentive functions.

Keywords: Shared control, Urban mobility, Smart cities
Abstract: The large-scale allocation of public resources (e.g., transportation, energy) is among the core challenges of future Cyber-Physical-Human Systems (CPHS). In order to guarantee that these systems are efficient and fair, recent works have investigated non-monetary resource allocation schemes, including schemes that employ karma. Karma is a non-tradable token that flows from users gaining resources to users yielding resources. Thus far karma-based solutions considered the allocation of a single public resource, however, modern CPHS are complex as they involve the allocation of multiple coupled resources. For example, a user might want to trade-off fast travel on highways for convenient parking in the city center, and different users could have heterogeneous preferences for such coupled resources. In this paper, we explore how to optimally combine multiple karma economies for coupled resource allocations, using two mechanism-design instruments: (non-uniform) karma redistribution; and (non-unit) exchange rates. We first extend the existing Dynamic Population Game (DPG) model that predicts the Stationary Nash Equilibrium (SNE) of the multi karma economies. Then, in a numerical case study, we demonstrate that the design of redistribution significantly affects the coupled resource allocations, while non-unit exchange rates play a minor role. To assess the allocation outcomes under user heterogeneity, we adopt Nash welfare as our social welfare function, since it makes no interpersonal comparisons and it is axiomatically rooted in social choice theory. Our findings suggest that the simplest mechanism design, that is, uniform redistribution with unit exchange rates, also attains maximum social welfare.

Keywords: Decision-support for human operators, Smart Grid and Demand Response
Abstract: We consider a set of players who have to make a joint decision under uncertainty and learn via gradient dynamics how to reach the maximal profit for the coalition while the game is running and the revenue gets allocated to the players. The main result is the convergence of the intertwined learning-allocation dynamics to a stable solution which yields the maximal profit for the coalition and a stable allocation in the core of the asymptotic game.

Keywords: Semi-autonomous and mixed-initiative systems, Shared control, Decision-support for human operators
Abstract: We consider information design in settings where the designer has more information about a payoff relevant state than the players, but lacks knowledge about a payoff parameter, that is independent of the state and known by the players. We provide two formulations for this setting. In the first formulation, the designer assumes the payoff parameter is chosen by an adversary. In the second formulation, the designer solves the problem according to the average parameter value realization. We leverage the semi-definite program formulation of the information design problem for games with quadratic payoffs and Gaussian information structures to obtain the solutions for both formulations. In both formulations, the social welfare maximizing solution violates the obedience constraint for the realized parameter value. We numerically investigate the extent to which such violations affect the players' payoffs. We find that worst case solution yields a higher deviation from the obedience constraint on the average.

Keywords: Assistive devices, Assistive robotics
Abstract: As interactions with autonomous agents - ranging from robots in physical settings to avatars in virtual and augmented realities - become more prevalent, developing advanced cognitive architectures is critical for enhancing the dynamics of human-avatar groups. This paper presents a reinforcement-learning-based cognitive architecture, trained via a sim-to-real approach, designed to improve synchronization in periodic motor tasks, crucial for applications in group rehabilitation and sports training. Extensive numerical validation consistently demonstrates improvements in synchronization. Theoretical derivations and numerical investigations are complemented by preliminary experiments with real participants, showing that our avatars can integrate seamlessly into human groups, often being indistinguishable from humans.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems, Assistive devices, Shared control
Abstract: This study tackles the position tracking control of collaborative robots actuated via brushless DC (BLDC) motors under the constraint that the mechanical system dynamics contain uncertain parameters and joint velocity measurements are not available. Specifically a partial state feedback controller in conjunction with a novel robust velocity observer formulation that achieves semi-global uniform ultimate boundedness of the joint position tracking error is presented. The stability of the closed--loop system is guaranteed via Lyapunov like arguments. Numerical studies performed on a model of a two degree of freedom planar robot manipulators driven by BLDC motors are presented in order to illustrate the effectiveness of the proposed controller/observer couple.

Keywords: Comfort control in homes, Assistive devices, Smart infrastructure
Abstract: The emergence of e-textiles has facilitated impressive growth in the field of wearable technologies in recent years. Textile-based electrodes enabled long-period electrocardiogram monitoring within the health care field as they are less irritant to the skin and can be naturally embedded into the clothes. Beyond the specific health-oriented applications, physiological signals monitoring is also beneficial during other activities such as daily activities and sports. In this work, a system of a knitted t-shirt embedded with textile electrodes that targets measuring heart rate and respiration rate during various positions and activities is created. The performance of the implemented system is benchmarked against the performance of an off-the-shelf product using root mean square error. It has been shown that the proposed system can achieve good performance with heart rate respiration rate values close to that of the benchmark device.

Keywords: Assistive devices, Advanced control design-linear, non-linear, stochastic, large scale control systems, Biomedical implants
Abstract: Artificial pancreas (AP) is a cyber-physical system used by many people with type 1 diabetes. Current APs used in free living rely on feedback control based on continuous glucose monitoring data. Information on major disturbances (meals, exercise) to blood glucose concentration (BGC) is entered manually. A multivariable AP (mvAP) is developed for fully automated AP that can mitigate the effects of meals and physical activities by measuring and interpreting several physiological variables (feedforward control). It uses physiological variables and a personalized, recursively updated metabolic model of the user with a model predictive controller (MPC) to adjust insulin infusion flow rates. To ensure the accuracy of the model and a precise estimation of the model input disturbances, we use recursive least squares and unscented Kalman filter to concurrently estimate the model states and parameters. Support vector regression is utilized to forecast the disturbances over the future horizon to improve the accuracy of predicting future BGC values used by the MPC. Simulations and results of an exploratory clinical experiment are reported to illustrate the performance of the mvAP system.

Keywords: Assistive devices, Assistive robotics, Exoskeletons
Abstract: This paper presents a safety controller for preventing ankle sprains, one of the most common musculoskeletal injuries, via a non-conservative control barrier function that incorporates uncertain preview/predictions of human ankle motion primitives. Specifically, our approach predicts the confidence interval of human ankle joint inversion/eversion angle trajectories during the stance phase using a probabilistic movement primitive (ProMP) and accounts for the prediction uncertainty when ensuring safety. Safety is guaranteed with high probability using an uncertain-preview control barrier function (uPrev-CBF) framework where the ProMP predictions, though uncertain, are leveraged as previewable disturbances to obtain a less conservative safety controller. Results from the ProMP model learning, based on data from four healthy subjects, demonstrate that the model effectively captures the variability in ankle inversion/eversion motion with high confidence. In addition, simulation results of the proposed safety controller based on uPrev-CBF show that ankle angle limit violations can be eliminated, effectively preventing ankle sprains.

Keywords: Assistive robotics
Abstract: In this study, we present the design and numerical implementation of a novel force/torque observer formulation, for collaborative robotic tasks that might require human interaction. Specifically a dynamically self-adjusting robust observer is introduced to ensure practical estimation of the end effector force/torque values of a robotic manipulator. The stability of the observer is guaranteed via Lyapunov based arguments and simulation studies implementation on a two link robotic manipulator are presented to demonstrate proof-of-concept.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems, Exoskeletons, Assistive devices
Abstract: People post-stroke walk with unnatural gait patterns due to reduced propulsion and muscle weakness in their affected leg. Powered exoskeletons can provide gait assistance to improve their walking speed and endurance. However, these robotic devices usually provide assistive torques to emulate healthy gait patterns and biological joint moments that do not directly translate to improving muscle capacity and propulsion. Alternatively, applying safe kinematic perturbations about joints at discrete instances in the step cycle can aid to target muscles, such as the ankle plantarflexors or the soleus muscle, and thus, improve propulsion during walking. This paper develops two closed-loop controllers for a motorized ankle-foot orthosis with a cable-driven mechanism that strategically perturbs the ankle joint during the loading phase of walking, which is the region where the soleus muscle is naturally most active. First, a nonlinear tensioning controller is developed to build tension in the cable mechanism and prevent slackness in early stance phase. The tensioning controller is activated at heel strike and tracks a desired electric motor trajectory generated by an admittance model. Then, a joint perturbation controller is developed to apply kinematic deviations (perturbations) to the ankle joint in the mid-late stance (loading) phase of walking to evoke changes in the soleus muscle activity. A Lyapunov-based stability analysis is developed independently for each controller ensuring exponential tracking in their respective regions of the step cycle. The controllers were implemented during a treadmill walking experiment at a comfortable, moderate speed in one able-bodied individual to establish feasibility of the methods.

Keywords: Spacecraft control
Abstract: This work proposes a human-in-the-loop control structure that consists of adaptive control allocation and a sliding mode controller. The control structure compensates for the effects of an unknown control effectiveness matrix and model uncertainties. The human operator is modeled as a general transfer function, and the output of the human operator is used in the control signal. Lyapunov stability analysis is provided to prove the stability of the closed-loop system. Also, simulation results are presented to show that the proposed adaptive method results in an asymptotic command tracking performance on a vehicle model that is operated with eight thrusters.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems
Abstract: Model Predictive Control (MPC) is becoming a popular control method for teleoperation due to its ability to ensure safety constraints. However, tuning MPC is a non-intuitive process that requires significant expertise and effort. In this work, we propose a method for auto-tuning a model predictive controller in bilateral teleoperation settings. We use the Bayesian Optimization algorithm (BO) to seek the optimal weights of the MPC cost function for precise teleoperation. Our simulations and experiments show the effectiveness of the proposed tuning method.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems
Abstract: Traditional event-triggering architectures rely on the knowledge of the structure of continuous- or discrete-time control laws. However, not every control law necessarily possesses a known structure in a closed form. This is especially the case when they are generated by humans, machine learning or artificial intelligence algorithms, or computational optimization or model predictive control methods. Motivated by this observation, the contribution of this paper is to make the first attempt at addressing this scientific gap, where we propose a novel control structure-agnostic event-triggering framework that relies solely on open-loop system dynamics. In addition to the presented system-theoretical analyses of the proposed framework, two illustrative numerical examples respectively involving a human subject and a reinforcement learning strategy are provided to demonstrate the efficacy of our contribution.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems
Abstract: This paper investigates a novel control framework for strategic Artificial Intelligence (AI) in human interactions. We leverage the Experience-Weighted Attraction (EWA) model, a widely used method for capturing human learning dynamics. EWA incorporates experiences to influence future choices through ``attraction values" assigned to different actions. By treating these attraction values as the system state, we formulate the interaction between the AI and human as a stochastic control problem. This approach allows the AI to strategically influence the human's behavior by manipulating the environment or offering incentives that alter the attraction landscape, even under conditions of partial knowledge about the human agent's learning process. Our framework contributes to the field of human-AI interaction by providing a novel control method driven by the dynamics of human learning through EWA.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems, Semi-autonomous and mixed-initiative systems
Abstract: With the expansion of mobile robot applications, their interactions with humans and their environments have significantly increased, emphasizing the need for enhanced maneuverability and improved environmental detection systems. This work presents the design, development, and control of a mobile robot implemented as a Cyber-Physical System (CPS). The robot communicates wirelessly within networked environments and is equipped with intelligent environmental detection systems compatible with the Robot Operating System (ROS). The robot is capable of moving independently in three degrees of freedom using omnidirectional wheels for high maneuverability. Design of an algorithm that enables the robot to navigate in crowded environments, avoiding dynamic human objects while reaching its target is presented. This algorithm calculates weighted motion factors by considering obstacles, gaps, and targets. Different experimental test scenarios are performed for proof of concept.

Keywords: Remote operation of robotic teams, Advanced control design-linear, non-linear, stochastic, large scale control systems
Abstract: In this work, two-input-two-output (TITO) unstable systems with time delays are considered. In particular, the focus is on haptic systems, where the human operator acting on the haptic interface and the virtual object are physically separated by a communication network. It is assumed that the underlying plant contains coupled time delays (in both channels) and additional delays arise from the network. It is shown that an extension of the modified Smith predictor based control with decoupler and a reference filter stabilizes the feedback system under delay uncertainties.

Keywords: Cognitive control, Ethics, Advanced control design-linear, non-linear, stochastic, large scale control systems
Abstract: This paper presents a control-theoretic model of social phenomena in human learning settings for educational infrastructure design. Our focus is to capture the decisions by human agents on allocating learning resources through social interactions using receding horizon control (RHC) policies. By simulation, we demonstrate preliminary results on the trajectories of the agents’ cognitive development, and show the reachable sets of the proposed system. The model parameters of the proposed system can be estimated using data from surveys and wearable sensors, capturing real-time interactions and preferences. The results of this model can help explore the implications for adaptive ambience infrastructure in educational environments by leveraging feedback from resource allocation dynamics to foster equitable learning outcomes.

Keywords: Smart infrastructure, Smart cities, Ethics
Abstract: This study introduces a new approach for modeling preference-aware human spatial behavior in cyber-physical-human systems using Graph Neural Networks (GNN) and Reinforcement Learning (RL). Current models often overlook the causality and impact of factors influencing preferences. Our approach utilizes GNN for its advanced handling of spatial data, capturing physical, social, and environmental features and their human perception. Integrated with RL, the model dynamically adapts to changes in the surrounding environment. We illustrate the approach in an educational conference room setting, comparing student behavior simulations with and without preferences. The results indicate that preference incorporation leads to significantly more realistic simulations.

Keywords: Smart cities, Smart infrastructure, Connected buildings
Abstract: The energy consumption of a building is significantly influenced by occupant behaviour. This paper presents an agent-based model that accounts for social interactions among occupants and quantitatively portrays the evolution process of their energy use patterns due to social norms. Several important factors influencing occupants’ total energy consumption have been identified and examined within the model, including individual’s energy use intensity (EUI) distribution and the introduction of energy-saving ambassadors into a scale-free social network. Results indicate that up to 40% of energy savings can be achieved through specific strategies, providing valuable insights for promoting sustainable practices among occupants in real world.

Keywords: Smart infrastructure, Smart cities, Ethics
Abstract: Cyber-physical-social infrastructure systems extend traditional cyber-physical systems by integrating human-infrastructure interactions and control theory to meet human-centered objectives (e.g., sociability and productivity). The change of human roles in the system from operators to users involves defining social objectives, modeling human behaviors, linking human behaviors to social objectives, and actuating the environment to achieve desired social outcomes. Within this feedback cycle, the mapping from human behaviors to their corresponding social goals is challenging since they are intangible. Towards this end, we propose a dataset that lays the groundwork for extracting social embeddings of human behaviors in a privacy-preserving way. To provide a baseline evaluation of these contextualizable human activities, we benchmark 7 human activity recognition algorithms. The results demonstrate that the intricacy of these human activities still remains unaddressed by state-of-the-art algorithms.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems, Comfort control in homes, Semi-autonomous and mixed-initiative systems
Abstract: Applying cyber-physical system (CPS) to buildings plays an important role for the development of energy efficient buildings. In this paper, we aim to propose a sampled-data observer to monitor the room temperature of building-CPSs described by a high-dimensional semilinear Boussinesq equation. For this, we first investigate well-posedness of the considered equation. A Luenberger-type sampled-data observer is then designed based on the output measurements. Subsequently, we derive sufficient conditions for ensuring exponential stability of the resulting observer error systems by using Lyapunov-Krasovskii method. Finally, we present a numerical example to verify our results.

Keywords: Decision-support for human operators, Smart infrastructure, Assistive devices
Abstract: This paper presents a modified Friedkin-Johnsen dynamic social learning model to quantify equitable learning metrics in preschool classrooms. The model parameters were estimated using experimental data collected from a longitudinal study exploring social learning in preschool classrooms. The model serves as an exploratory tool for the participatory design of cyber-physical-human technology in educational settings.

Keywords: Cognitive control, Smart cities
Abstract: The random utility model (RUM) is a fundamental notion in studies of human decision-making. However, RUM relies on the calibration of its choice function's weight parameter, usually interpreted as a rationality parameter, resulting in a case dependence that undermines both interpretability and predictability of choices across experimental settings. We addressed this limitation by normalizing utilities in RUM and deriving a new choice parameter β, independent of case-specific prospects. Drawing from a novel interpretation of β in terms of the lowest perceived probability of unlikely events, we conducted an experimental survey in Swedish universities to infer β distributions, capturing the variability of probability perception among decision-makers. We tested these statistical models for β on two independent datasets exploring the framing effect. The results showed that the predictions align with the observed experimental data (Pearson's correlation greater than 94%), thereby indicating that the novel characterization of the choice parameter strengthens the predictive capabilities of RUM.

Keywords: Semi-autonomous and mixed-initiative systems, Cognitive control, Potential impact of automation and open problems
Abstract: With the advancement of big data and the latest artificial intelligence technologies, cyber systems are increasingly equipped to collaborate with humans, enabling collaborative interactions and decision-making, thus forming Cyber-Physical-Social Systems (CPSS). As a typical complex system or System of Systems (SoS), the analysis and design of CPSS necessitate the support of systems engineering methodologies, including architecture and methodological frameworks. However, the existing CPSS architectures and methodologies, tailored for specific domains, exhibit distinct industry and domain focuses. To address the challenges of integration and interoperability among diverse CPSS architectures, this paper leverages a specially constructed meta-model to conduct a comparative analysis with existing standard CPSS meta-models. Subsequently, it undertakes an interoperability analysis with typical CPSS architectures and explores the dimensions within these architectures. This approach aims to foster a new perspective for the integration, analysis, and design of the complex, heterogeneous systems characteristic of CPSS.

Keywords: Cognitive control, Shared control, Decision-support for human operators
Abstract: In this paper, we conduct on-site group experiments of a multi-alternative decision making game. The objective is to capture a change of socially chosen strategies (i.e., social diffusion) by introducing a couple of committed minority bots and designing the balance between internal/external conformity biases, i.e., inertia and social coordination. We demonstrate the impact of the number of committed minority bots through the experiments. Furthermore, we reveal possible bounded rationality of the subjects from a gap between the designed conformity biases and estimated ones in the multi-alternative decision making game.

Keywords: Cognitive control
Abstract: The way we move carries significant information, reflecting attributes of our environment as well as inner states such as intentions and expectations. A major challenge in measuring this information is the inherent variability in movement kinematics, which can differ from one trial to another and from one individual to another. We introduce a novel information-based framework - kinematic coding - to quantify how information is coded in movement kinematics at the single-subject, single-trial level. This framework demonstrates potential for studying both the encoding of information in movement kinematics during action execution and the readout of this information by others during social interaction.

Keywords: Cognitive control, Advanced control design-linear, non-linear, stochastic, large scale control systems
Abstract: We present a neuro-inspired control framework for autonomous robots that integrates an artificial emotion of fear, drawing inspiration from LeDoux’s dual-pathway hypothesis. To replicate the "Low Road" pathway, our system comprises proxies for the thalamus, implemented as a nonlinear filter; the amygdala, modeled as a Soft Actor-Critic (SAC) reinforcement learning agent; the brainstem, orchestrated through a Nonlinear Model Predictive Controller (NMPC). The NMPC’s parameters are adjusted by the amygdala, enabling it to generate control inputs to actuate the robot. Our preliminary results demonstrate that the robot exhibits a better adaptive behavior than a standard NMPC in both static and dynamic environments with obstacles characterized by different hazard levels.

Keywords: Cognitive control
Abstract: In this study, we conducted a series of computer-based audio-visual sensory cue integration experiments to validate the Minimum-Variance Unbiased Estimator (MVUE) model, within the framework of cyber-physical human systems. Initial tests were instrumental in tuning the parameters for optimal cue integration, necessitating adjustments to the model. Despite exhaustive testing with multiple human participants, our results did not support the MVUE model. These findings highlight the need for alternative methodologies in future work to achieve robust validation in cyber-physical human systems. Future research will focus on enhancing model accuracy and reliability to better understand human interaction within such systems.

Keywords: Advanced control design-linear, non-linear, stochastic, large scale control systems, Cognitive control, Intelligent road transportation
Abstract: Autonomous agents operating around human actors must consider how their behaviors might affect those humans, even when not directly interacting with them. To this end, it is often beneficial to be predictable and appear naturalistic. Existing methods to address this problem use human actor intent modeling or imitation learning techniques, but these approaches rarely capture all possible motivations for human behavior or require significant amounts of data. In contrast, we propose a technique for modeling naturalistic behavior as a set of convex hulls computed over a relatively small dataset of human behavior. Given this set, we design an optimization-based filter which projects arbitrary trajectories into it to make them more naturalistic for autonomous agents to execute while also satisfying dynamics constraints. We demonstrate our methods on real-world human driving data from the inD intersection dataset.

Keywords: Assistive robotics
Abstract: Research in the field of child-computer interaction shows that children perceive voice assistants and social robots as friends. The current study focuses on how children perceive robots and the nature of their questions during these interactions. A total of 35 children ages 6-9 and tested children in an individual room in their schools. First, children were introduced to the NAO robot and asked about their perceptions (e.g., Can you be friends with the robot?). Then, we presented children with various animals and objects to examine whether they would ask the robot information-seeking questions. To simulate smooth question-answer interactions, we developed a Wizard of Oz teleoperation interface using a Python GUI. We expect age differences in children’s perceptions and question-answer exchanges with the robot and differences in children’s question-asking behavior depending on the quality of answers provided by the robot.

Keywords: Assistive robotics
Abstract: This project investigates whether power and identity threats from social robots would trigger individuals’ willingness to act collectively on behalf of humanity, i.e., humanitarian collective action (HCA) intentions. We argue that social robots can threaten two basic human needs – the need for control and distinctiveness – when their autonomy and/or similarity to humans are salient. Our pilot correlational study provides initial evidence for the hypotheses and indicates both identity and power threats to predict higher levels of humanitarian collective action.

Keywords: Decision-support for human operators, Cognitive control, Assistive robotics
Abstract: The present study aims to compare methods that disrupt biological motion and examine the factors that influence the perceived mechanicalness of movement stimuli. We disrupted the biological motion of animated characters using four different methods that came in two types: direction-dependent or frame-dependent. The results emphasize the role of direction dependency in motion and the sinusoidal curve of the motion trajectory in biological motion perception and suggest that the identity of the agent and the number of limbs used in performing an action influence the perceived mechanicalness.

Keywords: Assistive robotics
Abstract: As the older population grows, it is crucial for their well-being to maintain a degree of independence and avoid feelings of loneliness by living in their own homes. This study explored the attitudes of older adults and their caretakers towards social robots, employing sentiment and thematic analysis to determine if these robots can serve as tools for environmental enrichment in healthy aging.

Keywords: Assistive robotics
Abstract: Building on the Intergroup Contact Theory, we tested the effectiveness of contact manipulation, whereby humans have a brief positive interaction with a social robot, to promote more positive attitudes towards robots in general. Across multiple studies, we showed that having a positive interaction with a member of the outgroup Robots can positively influence attitudes, feelings, and beliefs about robots as a group, as well as some uncontacted human outgroups.

Keywords: Assistive robotics, Comfort control in homes
Abstract: This study addresses the challenge of integrating social norms into robot navigation, which is essential for ensuring that robots operate safely and efficiently in human-centric environments. Social norms, often unspoken and implicitly understood among people, are difficult to explicitly define and implement in robotic systems. To overcome this, we derive these norms from real human trajectory data, utilizing the comprehensive ATC dataset to identify the minimum social zones humans and robots must respect. These zones are integrated into the robot's navigation system by applying barrier functions, ensuring the robot consistently remains within the designated safety set. Simulation results demonstrate that our system effectively mimics human-like navigation strategies, such as passing on the right side and adjusting speed or pausing in constrained spaces. The proposed framework is versatile, easily comprehensible, and tunable, demonstrating the potential to advance the development of robots designed to navigate effectively in human-centric environments.