Robots are traditionally confined to controlled environments such as factories, where human interactions are limited. However, the demand for robots that are capable of collaborating with humans is increasing. To achieve symbiosis, integrating the physical flexibility and environmental adaptability of living organisms into robotic systems is crucial. An example of such a robot is a biohybrid robot driven by three-dimensional (3D) cultured skeletal muscle cells. These muscle cells, which are composed of myoblasts and an extracellular matrix (ECM), contract and generate force in response to external stimuli. The standardization of such 3D-cultured skeletal muscle cells is essential for practical applications. However, their complete standardization has not yet been achieved. The contractile force of 3D-cultured skeletal muscle cells produced via 3D printing is still insufficient for practical applications as actuators in biohybrid robots. In a previous study, we developed a simple fabrication method for 3D-cultured skeletal muscle cells. These bio-cultured artificial muscle (BiCAM) cells can control the shape and cell alignment of tissues. Differences in the composition of an ECM have been suggested to affect the contractile force of 3D skeletal muscle tissues; however, their impact on the response characteristics remains poorly understood. In this study, we investigated how the ECM composition influences the contractile force of 3D skeletal muscle cells in biohybrid robots as a step toward their eventual standardization. Compared with tissues cultured under MF conditions, in which electrically induced contraction was previously confirmed, tissues cultured under CM conditions exhibited an approximately two-fold greater contractile force at voltage amplitudes of 10 and 30 V. Furthermore, the fabrication success rate was 100 under CM conditions but only 62.5-70 under other ECM conditions. In contrast, although CM tissues generated larger forces, tissues cultured under MgF and CMg conditions exhibited higher-frequency response. These findings demonstrated that the BiCAM is a viable actuator and offers new possibilities for the design of biohybrid robots.

Robotic hands and upper-limb prostheses stand at the intersection of mechanical design, intelligent control, 2 and human experience, representing one of the most challenging frontiers in robotics and human-machine 3 interaction. Despite remarkable progress in actuation, sensing, and learning, achieving manipulation 4 capabilities that are robust, adaptable, and intuitive remains a fundamental challenge. With their remarkable 5 and efficient manipulation skills, the human hand can serve as an inspiration to further advance the 6 development of robotic hands. Therefore, we believe that the human perspective is key to address the 7 robotic challenges we are facing in manipulation. Robotic hands are inspired by, interact with, assist, or 8 replace human hands, and must therefore be understood not only as technical systems, but also as embodied The four contributions selected in this special issue reflect this perspective by spanning learning paradigms, 13 dexterous manipulation, clinical prosthetics, and human-centered robotic assistance, as depicted in Figure 1.14 Together, they illustrate how advances in robotic hands increasingly emerge from the integration of human 15 factors with robust engineering and learning-based approaches.On the topic of robot learning, Welte and Rayyes provide a comprehensive survey of interactive imitation

Previous meta-analyses show that social robots aid learning but were often limited in scope or grouped diverse control conditions together. This meta-analysis examined learning outcomes, focusing on control condition type. We retrieved 146 studies (Google Scholar and reference searches) where a physical social robot was used for training cognitive skills, comprising 183 post-test effect sizes between the robot and the control group, and 372 pre-post effect sizes. Analysis of the 78 studies with control groups indicated that robots generally improved learning, most notably when compared to a no-training control group (d = 0.75). Comparing robots to human teachers yielded an overall positive effect (d = 0.31), although effect sizes varied widely. This variability was explained by the robot’s role: robots in a co-teaching capacity showed a strong positive effect (d = 0.88), while robots replacing the teacher showed no benefit (d = −0.06). LLM-based sentiment analysis indicated that papers from outside Europe received higher positivity scores when describing the robots. We conclude that the effect size is influenced by the robot implementation and the control condition chosen.

IntroductionLiDAR–camera fusion systems are widely used in robotic localization and perception, where accurate extrinsic calibration is crucial for multi-sensor fusion. During long-term operation, extrinsic parameters can drift due to vibration and other disturbances, while target-based recalibration is inconvenient in the field and targetless approaches often suffer from highly non-convex objectives and limited robustness in challenging outdoor scenes.MethodsWe propose a targetless LiDAR–camera extrinsic calibration method by minimizing a semantic distribution consistency risk on SE(3). We align semantic probability distributions from the two sensing modalities in the image domain and freeze the pixel sampling measure at an anchor pose, so that pixel weighting no longer depends on the current extrinsic estimate and the objective landscape remains stable during optimization. On top of this anchor-fixed measure, we introduce a direction-aware weighting strategy that emphasizes pixels sensitive to yaw perturbations, improving the conditioning of rotation estimation. We further use a globally balanced Jensen–Shannon divergence to mitigate semantic class imbalance and enhance robustness.ResultsExperiments on the KITTI Odometry dataset show that the proposed method reliably converges from substantial initial perturbations and yields stable extrinsic estimates.DiscussionThe results indicate that the method is promising for maintaining long-term LiDAR–camera calibration in real-world robotic systems.

This paper presents a conceptual framework for the design of personalized persuasive conversational agents to support positive behavior change. This paper leverages key theoretical models to understand the determinants of behavior change and explores how these models can inform the design of personalized conversational agents to enhance their effectiveness in healthcare interventions. The role of personalization in dialogue-based intervention is discussed, emphasizing the importance of adaptation to individual characteristics, preferences, and contexts. The potential of persuasive language generation is also examined, highlighting its ability to create more engaging and impactful behavior change strategies. Finally, the paper proposes a layered framework that explicitly links behavioral models, user personalization, and persuasive language generation, and discusses future research directions for integrating this framework in social robots’ interventions for behavior change in healthcare.

Cloth unfolding and folding are fundamental tasks in autonomous robotic cloth manipulation as Physical AI. Driven by recent advances in deep learning, this area has developed rapidly in recent years. This review aims to systematically identify and summarize current progress in deep learning-based cloth unfolding and folding. Following the Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 41 relevant papers from 2019 to 2024 were selected for analysis. We examines various factors influencing cloth manipulation and find that, while current methods show impressive performance, several challenges remain unaddressed. These challenges include irregular cloth sizes and diverse initial garment states. Concerning datasets, there is a need for improved real-world data collection systems and more realistic cloth simulators, and the Sim2Real gap must be carefully considered. Additionally, the review highlights the importance of incorporating multi-modal sensors into current platforms and the emergence of novel primitive actions that enhance performance. The need for more consistent comparison metrics is emphasized, and strategies for addressing failure modes are discussed to further advance the field. From an algorithmic perspective, we reorganize existing learning methods into six learning and control paradigms: perception-guided heuristics, goal-conditioned manipulation policies, predictive and model-based state representation methods, reward-driven reinforcement learning over primitive actions, demonstration-driven skill transfer methods, and emerging large language model-based planning methods. We discuss how each paradigm contributes to unfolding and folding, their respective strengths and limitations, and the open problems that arise. Finally, we summarize the remaining challenges and provide future perspectives for physical AI.

Gaze is a fundamental aspect of non-verbal communication in human interaction, playing an important role in conveying attention, intentions, and emotions. A key concept in gaze-based human interaction is joint attention, the focus of two individuals on an object in a shared environment. In the context of human-robot interaction (HRI), gaze-following has become a growing research area, as it enables robots to appear more socially intelligent, engaging, and likable. While various technical approaches have been developed to achieve this capability, a comprehensive overview of existing implementations has been lacking. This scoping review addresses this gap by systematically categorizing existing solutions, offering a structured perspective on how gaze-following behavior is technically realized in the field of HRI. A systematic search was conducted across four databases, leading to the identification of 28 studies. To structure the findings, a taxonomy was developed that categorizes technological approaches along three key functional dimensions: (1) environment tracking, which involves recognizing the objects in the robot's surroundings; (2) gaze tracking, which refers to detecting and interpreting human gaze direction; and (3) gaze-environment mapping, which connects gaze information with objects in the shared environment to enable appropriate robotic responses. Across studies, a distinction emerges between constrained and unconstrained solutions. While constrained approaches, such as predefined object positions, provide high accuracy, they are often limited to controlled settings. In contrast, unconstrained methods offer greater flexibility but pose significant technical challenges. The complexity of the implementations also varies significantly, from simple rule-based approaches to advanced, adaptive systems that integrate multiple data sources. These findings highlight ongoing challenges in achieving robust and real-time gaze-following in robots, particularly in dynamic, real-world environments. Future research should focus on refining unconstrained tracking methods and leveraging advances in machine learning and computer vision to make human-robot interactions more natural and socially intuitive.

Robotic fleet management systems are increasingly vital for sustainable operations in agriculture, forestry, and other field domains where labor shortages, efficiency, and environmental concerns intersect. We present FORMIGA, a fleet management framework that integrates human operators and autonomous robots into a collaborative ecosystem. FORMIGA combines standardised communication through the Robot Operating System with a user-centered interface for monitoring and intervention, while also leveraging large language models to generate executable task code from natural language prompts. The framework was deployed and validated within the FEROX project, a European initiative addressing sustainable berry harvesting in remote environments. In simulation-based trials, FORMIGA demonstrated adaptive task allocation, reduced operator workload, and faster task completion compared to semi-autonomous control, enabling dynamic labor division between humans and robots. By enhancing productivity, supporting worker safety, and promoting resource-efficient operations, FORMIGA contributes to the economic, and environmental dimensions of sustainability, offering a transferable tool for advancing human-robot collaboration in field robotics.

In the era of artificial intelligence and rapidly advancing robotics, the field of Human–Robot Interaction (HRI) has taken center stage across multiple domains, including psychology. From a psychological perspective, it is therefore essential to deepen our understanding of the factors that shape the quality of these interactions and their implications. This emphasis also aligns with the principles of Industry 5.0, which prioritize human well-being and use technologies to promote sustainable progress. The present study employs an exploratory mixed-method approach and aims to examine perceptions of warmth, competence and discomfort with the Furhat social robot in a psychological assessment setting. Specifically, we investigated young adults’ interactions with the Furhat social robot while it administered the Depression, Anxiety and Stress Scale (DASS-21). Following the interaction, the participants completed the short version of the Robot Social Attributes Scale (RoSAS-SF) to assess perceived warmth, competence and discomfort, and provided qualitative feedback regarding their interactional experiences and acceptance of the robot. The findings provide preliminary insights into the respondents’ perceptions of and openness toward robot-administered psychological screening, suggesting that the Furhat social robot may have potential as an assistive tool in mental health assessment contexts. These results highlight the need for further research with larger samples to examine the role of social robots in psychological practice more comprehensively.

Wetware Network-Based Artificial Intelligence (WNAI) introduces a new approach to robotic cognition and artificial intelligence: autonomous cognitive agents built from synthetic chemical networks. Rooted in Wetware Neuromorphic Engineering, WNAI shifts the focus of this emerging field from disembodied computation and biological mimicry to reticular chemical self-organization as a substrate for cognition. At the theoretical level, WNAI integrates insights from network cybernetics, autopoietic theory and enaction to frame cognition as a materially grounded, emergent phenomenon. At the heuristic level, WNAI defines its role as complementary to existing leading approaches. On the one hand, it complements embodied AI and xenobotics by expanding the design space of artificial embodied cognition into fully synthetic domains. On the other hand, it engages in mutual exchange with neural network architectures, advancing cross-substrate principles of network-based cognition. At the technological level, WNAI offers a roadmap for implementing chemical neural networks and protocellular agents, with potential applications in robotic systems requiring minimal, adaptive, and substrate-sensitive intelligence. By situating wetware neuromorphic engineering within the broader landscape of robotics and AI, this article outlines a programmatic framework that highlights its potential to expand artificial cognition beyond silicon and biohybrid systems.