The sustainability of low-power mobile sensors is severely challenged by their limited battery capacity, and while simultaneous wireless information and power transfer (SWIPT) is a promising solution, its efficiency suffers dramatically under the uncertainty inherent to mobile three-dimensional (3D) aerial-to-ground environments. This work addresses the critical need for robust and efficient SWIPT under dynamic uncertainty by proposing a novel sensing-assisted SWIPT framework based on a unique hybrid learning algorithm. Our approach first formulates a two-layer optimization problem that rigorously couples a sensing layer, characterized by the Posterior Cram´er-Rao Bound (PCRB), with a SWIPT resource allocation layer. For the sensing layer, the core novelty is a learning-based Kalman Filtering (KF) estimator that merges the interpretative stability of model-based filtering with the adaptive power of neural networks to learn complex, nonlinear mobility patterns. We then prove that minimizing the estimator’s unsupervised loss is mathematically equivalent to minimizing the PCRB, ensuring convergence to optimal sensing without ground-truth supervision. This high-fidelity state information drives a decision-making learning model that adaptively optimizes beamforming, transmit power, and power splitting for the SWIPT resource allocation layer, forming a closed-loop hybrid learning system that continuously reinforces sensing and SWIPT performance. Extensive simulations demonstrate that our framework significantly outperforms benchmark methods in sensing accuracy, communication rate, and energy harvesting, validating its effectiveness in dynamic mobile environments.

Affective robotics centers on recognizing emotional states and generating artificial emotions through embodied robotic systems. This paper surveys the current state of the field, with a particular focus on bodily expressed emotion—both in recognizing affect through body movements and postures, and in generating movement that is parsed as affect by human observers. Framed through the lens of the performing arts, this examination provides insights into the expressive potential of robots, motivates key open questions, and highlights challenging problems, as presented through an art-inspired case study and foundational background material. A close engagement with the performing arts suggests intense malleability and diversity of bodily expression, challenging some of the field's prevailing goals—such as designing generally “happy” robotic movement—and emphasizing the importance of variables such as context and interactional intent. The paper concludes by proposing future directions for bodily expressed affective robotics that integrate advances from both robotics and the performing arts.

How does the implementation of robotic systems impact patient care, therapist roles, and clinical workflows in rehabilitation care? This study presents preliminary findings on the real-world implementation of the Lokomat® robotic gait therapy device in a rehabilitation hospital, focusing on the experiences of clinicians. Through qualitative methods, including interviews and direct observations, the study identifies both anticipated benefits and unforeseen challenges. Participants expressed optimism about the device's ability to extend therapy duration, improve patient outcomes, and alleviate therapists' physical workloads. However, operational realities such as time- and labour-intensive setups and staffing constraints revealed unexpected costs, both in terms of workload and logistics. The findings highlight the need for holistic implementation approaches that address both patient-centric benefits and clinician-centric challenges to ensure the sustainable adoption of robotic systems in healthcare.

The evolution of artificial intelligence (AI) has profoundly reshaped human-AI interactions, transitioning from rule-based systems to advanced machine learning algorithms capable of nuanced tasks. This paper introduces a novel architecture for embodied AI in human-robot interaction (HRI), designed to foster meaningful, long-term relationships. Central to the approach is the integration of persistent memory, enabling AI systems to recall past interactions, build continuity, and deliver personalised, contextually aware responses. Additionally, the architecture incorporates emotional intelligence to allow AI to recognise, interpret, and respond to human emotions, enhancing emotional engagement and trust. Key contributions of this paper include a unified approach that links attention mechanisms, memory, and generative AI to support dynamic, context-sensitive interactions; a focus on embodiment as a critical factor in HRI, highlighting its role in grounding interactions within physical and social contexts; and theoretical and practical advancements that extend existing attention-based systems by incorporating persistent memory and emotional intelligence for deeper human-AI connections. This work lays the foundation for developing AI systems capable of forming enduring, meaningful relationships with users, setting a new benchmark for human-centered AI design.

As robots are becoming increasingly common within social settings, understanding the drivers of effective Human-Robot Interaction (HRI) becomes crucial. One factor emerging as a potential key variable in HRI is social presence, which is the extent to which a person feels connected to, or aware of, the presence of another. In the context of HRI, social presence has been identified as a factor that shapes human responses to robots, impacting outcomes such as attachment, trust, and social influence. Social presence itself appears to be influenced by factors including the nature of the embodiment. However, HRI research examining social presence is limited by the lack of consistent operationalization of social presence and conflation with the variance in embodiment of the robot. The current pilot study is designed to tease apart embodiment and social presence by exploring the relative impacts of interactions with two matched representations of a social robot (physical and virtual), finding that even controlling for size, proximity and response mechanisms, physical embodiment is associated with higher reported social presence. The study also identified both consistencies and inconsistencies in the measurement of social presence across assessments. The findings provide a foundation for further study into social presence in HRI, by clarifying mechanisms of quantifying and experimentally manipulating social presence, allowing insight into ways in which this factor drives HRI.

In the early days of the Open Source Robotics Foundation, a lesser-known project aimed to design an “app-able robot”, leading to the creation of the “Capabilities” package for the Robot Operating System (ROS). Over a decade later, formulating robot capabilities remains a significant technical hurdle in bringing robots from the lab into everyday life. This paper introduces Capabilities2, a successor to the original Capabilities package, now reimagined for ROS2. Capabilities2 enhances the original design by enabling advancements in skill-based control techniques and offering a more efficient, extensible framework for defining and utilising robot capabilities. We delve into its application in new real-world scenarios, with a particular focus on human-robot interactions and the deployment of collaborative mobile robots in human-centric environments. Capabilities2 addresses challenges in implementing intuitive, collaborative robots by introducing an abstracted database handler, an object-relational mapping for capability models, and a plugin architecture for capability execution. These features support dynamic capability representation, runtime adaptability, and integration with modern AI techniques for skill-based task planning. By providing a standardised yet flexible framework, Capabilities2 reduces the integration effort required to develop top-level controls for real-world scenarios, facilitating rapid development and deployment. Our contributions include the reimplementation of the Capabilities package in ROS2, enhancements to support contemporary robotic applications, and demonstrations of new use cases enabled by Capabilities2. We believe that Capabilities2 significantly advances the field of robotics by equipping developers with tools to create more capable, adaptable, and interactive robots. Capabilities2 is available at https://github.com/CollaborativeRoboticsLab/capabilities2

Social Presence (SP) is a critical factor in human-robot interaction (HRI), influencing how users perceive and engage with social robots. Traditionally, SP has been conceptualized by focusing on the user's subjective perception of a robot's social capabilities, which we label, Attributed Social Presence (ASP). However, this perspective overlooks the inherent capabilities of robots that can engender a sense of presence, which we argue can be termed, Intrinsic Social Presence (ISP). In this paper we explicitly explore and distinguish between ASP and ISP, highlighting the limitations of solely focusing on attributed user perceptions in HRI. We address the challenges associated with the conceptual ambiguity of SP, the difficulties in benchmarking due to inconsistent definitions, and the lack of research examining robot presence modulation. To overcome these issues, we propose reframing SP as a multidimensional construct encompassing three interconnected domains. By emphasizing ISP and providing a practical framework for its implementation, this paper aims to enhance the development of adaptable, responsive robots capable of meaningful engagement. This approach bridges the gap between rigid robotic behaviour and the fluid, adaptive presence characteristic of human interactions, emphasizing that genuine trust and connection are integral to the full functionality and effectiveness of social robots.

Food waste management plays a vital role in maintaining a sustainable ecosystem, however, the presence of inorganic contaminants within food waste significantly hinders this potential. Robotic automation offers a promising solution to accelerate waste sorting, yet the diverse and unpredictable nature of contaminants poses major challenges to robotic perception and grasping. This benchmark study explores the feasibility and limitations of conventional robotic grasping systems, replicating real-world industrial conditions to highlight the complexities of food waste sorting. A comprehensive automated robotic grasping pipeline is introduced, integrating advanced 6D grasping pose detection, collision-free robotic arm motion planning, and effective grasping with three top-performing robotic end-effectors. Extensive experimental evaluations (up to 1500 robotic grasps) compare the performance of different gripper designs and the corresponding grasping strategies under three high-fidelity environmental scenes, providing valuable insights into the limitations of the current robotic system. Experiment results demonstrate the significant strengths of each gripper when dealing with objects of varying types or in different environments. This is critical for enhancing robotic sorting capabilities, particularly in advancing multimodal gripper technology.

Attention serves as a critical antecedent to social presence, which fundamentally influences acceptance, trust, and overall interaction quality in human-robot interaction (HRI). This paper investigates the development of a gaze modulation framework that enables robots to strategically influence human attention through two complementary Q-learning-based modules: Gaze-Garnering Modulation (GGM) and Gaze-Avoidance Modulation (GAM). To measure gaze feedback, we introduce a novel metric—the Dynamic Gaze Engagement Index (DGEI)—that integrates attention ratio with stationary gaze entropy (SGE) to evaluate not just the quantity but also the quality of visual attention. This feedback allows the system to continuously adapt to each individual’s unique attentional patterns and thresholds, providing personalised interaction. In two experiments, 20 participants interacted with a Pepper robot that dynamically adjusted its behaviours (lights, movements, and voice volume) based on real-time gaze feedback. Results demonstrated that GGM significantly enhanced gaze engagement, fostering strong mutual interaction, while GAM effectively redirected attention when appropriate, with participants reporting lower perceived gaze engagement in this condition. Post-experiment questionnaires using the "Psycho-behavioural Interaction - Perceived Attentional Engagement" section of the Networked Minds Social Presence Inventory (NMSPI) revealed significant differences between conditions (t(18)=2.47, p=0.0238), validating the attention modulation by each module and corroborating the behavioural observations. These findings underscore the importance of adaptive robotic behaviours in facilitating dynamic and unobtrusive interactions.

The aged care industry is under pressure from stressors including increasing resident numbers and difficulty meeting staffing requirements. Robots may be able to support the industry by filling many vital roles, however it is currently unclear how successful implementation of robots in aged care can occur, and detailed in situ assessment and mapping of robotic deployment in these settings is lacking. The current case study examines early-stage implementation of robots at an aged care home in Australia, assessing logistical, technical and person factors. Key facilitators and barriers to successful deployment are identified, including identifying needs and roles, health and safety issues and technical support. The findings illustrate the potential for robots in aged care and provide a blueprint for the steps needed for long-term effectiveness and commercial viability.

Human-robot interaction (HRI) spans many and diverse contexts, each varying in the preferred degree of social connection fostered therein. The design of robotic systems and selection of which robotic forms to use will thus benefit from an understanding of the factors by which social connection is enhanced or diminished. We address this fundamental problem through an exploratory study of role-taking, a core construct from structural social psychology. Through a laboratory experiment (N=86) in which participants interact with either human, humanoid robot, or non-humanoid robot partners, we examine variation in perceived role-taking accuracy, affection for the partner, and desire for continued interaction. Findings show that participants evaluate human interaction partners as more accurate role-takers than robots, but with variation between robotic types (humanoid vs non-humanoid) in relation to personality versus emotion. Sense of affection and desire for continued interaction are predicted by interpersonal factors rather than partner type or robotic form. We discuss possible reasons behind these preliminary findings and draw on them to formulate subsequent research questions. More broadly, we call for continued study at the intersection of HRI and structural social psychology, advancing both fields through the pairing.

This paper investigates the effects of using video motion magnification methods based on amplitude and phase, respectively, to amplify small facial movements. We hypothesise that this approach will assist in the micro-expression recognition task. To this end, we apply the pre-trained VGGFace2 model with its excellent facial feature capturing ability to transfer learn the magnified micro-expression movement, then encode the spatial information and decode the spatial and temporal information by Bi-LSTM model. Moreover, Grad-CAM is utilised to map the model and visually explain the operating mechanism of the spatio-temporal network. Experiments on the SMIC database confirm that the proposed framework significantly improves the micro-expression recognition rate compared to without video magnification (baseline) and other state-of-the-art methods.

The face is the most influential feature in any interaction. This study investigated the effect of robot faces in embodied interactions on a range of subjective and objective factors. The platform used to answer the research question was an interactive robotic art installation, incorporating a robot arm that could be presented with or without a face displayed on an attached screen. Participants were exposed to one of three conditions – the robot arm only, the robot arm with a static face displayed, and the robot arm with a dynamic face displayed. We used the Godspeed Questionnaire to determine whether the different embodiments would be perceived differently on measures of likeability, animacy, and safety before and after the interaction. We also measured how close participants stood to the robot and how much time they spent interacting with the robot. We found that the three embodiments did not significantly differ in time spent, distance, animacy, likeability, or safety before and after the interaction. This surprising result hints at other possible reasons that influence the success of a robot interaction and advances the understanding of the effect of faces in human-robot interaction.

A plethora of commercial social robots and social robotics startups have risen over the last few years. At a cursory glance, most such robots are merely conversational agents, essentially offering a similar or subset of the capabilities of a smart communication device embodied in a mobile/semi-mobile robotic platform. This raises the question of the efficacy of such an approach. In this paper, we explore embodiment using a social, in-the-wild interaction scenario, comparing a Human-Computer and Human-Robot context. A public site has been deliberately chosen to highlight the importance of conducting such user studies in unconstrained social settings as opposed to in controlled lab settings. Increasing evidence suggest the lack of generalizability of lab-based results in the wild, which we argue as a reason for misguided commercialization of social robots and their eventual commercial failures. The results have implications for the longterm commercial viability of such social robots.

Robots at home and work has been a key theme in science fiction since the genre began. It is only now that we see this come in to realization, albeit in very basic forms such as the robot vacuum cleaners and various entertainment robotic platforms. In this video we highlight a number of projects woven around the iRobot Create research robot platform and an embodied conversational agent called the Prosthetic Head - an installation work by Stelarc. We start the visual journey by taking a satirical look at some of the parallels between a commercial communication product and the Prosthetic Head. The journey then moves through telepresence robotics, gesture based robot human interaction. The robots featured in the video are driven by an attention and behavioral system. Finally, the video concludes with a preview of the "Swarming Heads" - an interactive installation.

On October 26th 2010, a unique HRI-artistic public experiment took place at the UsineC theater, in Montreal. It was the result of a many-months collaboration between the Montreal based lab hosting the [ VOILES | SAILS ] research-creation platform (Self-Assembling Intelligent Ligther-than-air Structures) and the well-known Australian artist Stelarc and his team, who work on artificial agents' embodiment and robotic behaviour modeling.

The Articulated Head (AH) is an artistic installation that consists of a LCD monitor mounted on an industrial robot arm (Fanuc LR Mate 200iC) displaying the head of a virtual human. It was conceived as the next step in the evolution of Embodied Conversational Agents (ECAs) transcending virtual reality into the physical space shared with the human interlocutor. Recently an attention module has been added as part of a behavioural control system for non-verbal interaction between robot/ECA and human.

Thinking Head project is a multidisciplinary approach to building intelligent agents for human machine interaction. The Thinking Head Framework evolved out of the Thinking Head Project and it facilitates loose coupling between various components and forms the central nerve system in a multimodal perception-action system. The paper presents the overall architecture, components and the attention system. The paper then concludes with a preliminary behavioral experiment that studies the intelligibility of the audiovisual speech output produced by the Embodied Conversational Agent (ECA) that is part of the system. These results provide the baseline for future evaluations of the system as the project progresses through multiple evaluate and refine cycles.

The main contribution of this paper arises from the development of a new framework, which has its inspiration in the mechanics of human navigation, for solving the problem of Simultaneous Localization and Mapping (SLAM). The proposed framework has specific relevance to vision based SLAM, in particular, small baseline stereo vision based SLAM and addresses several key issues relevant to the particular sensor domain. Firstly, as observed in the authors' earlier work, the particular sensing device has a highly nonlinear observation model resulting in inconsistent state estimations when standard recursive estimators such as the Extended Kalman Filter (EKF) or the Unscented variants are used. Secondly, vision based approaches tend to have issues related to large feature density, narrow field of view and the potential requirement of maintaining large databases for vision based data association techniques. The proposed Multi Map SLAM solution addresses the filter inconsistency issue by formulating the SLAM problem as a nonlinear batch optimization. Feature management is addressed through a two tier map representation. The two maps have unique attributes assigned to them. The Global Map (GM) is a compact global representation of the robots environment and the Local Map (LM) is exclusively used for low-level navigation between local points in the robot's navigation horizon.

In the past few years, there has been significant advancement in localization and mapping using stereo cameras. Despite the recent successes, reliably generating an accurate geometric map of a large indoor area using stereo vision still poses significant challenges due to the accuracy and reliability of depth information especially with small baselines. Most stereo vision based applications presented to date have used medium to large baseline stereo cameras with Gaussian error models. Here we make an attempt to analyze the significance of errors in small baseline (usually <0.1m) stereo cameras and the validity of the Gaussian assumption used in the implementation of Kalman filter based SLAM algorithms. Sensor errors are analyzed through experimentations carried out in the form of a robotic mapping. Then we show that SLAM solutions based on the extended Kalman filter (EKF) could become inconsistent due to the nature of the observation models used

With limited dynamic range and poor noise performance, cameras still pose considerable challenges in the application of range sensors in the context of robotic navigation, especially in the implementation of simultaneous localisation and mapping (SLAM) with sparse features. This paper presents a combination of methods in solving the SLAM problem in a constricted indoor environment using small baseline stereo vision. Main contributions include a feature selection and tracking algorithm, a stereo noise filter, a robust feature validation algorithm and a multiple hypotheses adaptive window positioning method in 'closing the loop'. These methods take a novel approach in that information from the image processing and robotic navigation domains are used in tandem to augment each other. Experimental results including a real-time implementation in an office-like environment are also presented

This paper proposes a method for simultaneous localisation and mapping (SLAM) in an indoor environment using stereo vision. Specially designed artificial landmarks distributed in the environment are observed and extracted from a camera image. The disparity map obtained from the stereo vision system is used to obtain the ranges to these landmarks. The main contribution of the paper is the formulation of the mathematical framework for SLAM for a robot moving on a planar surface among landmarks distributed in three dimensional space. The paper also presents the results of experiments conducted using a pioneer robot and a Triclops stereo vision system. It is demonstrated that accurate robot and feature locations can be obtained using the proposed technique.