Humanoid robots offer a promising solution to the growing burden of care for older adults. However, existing evidence on their applications for general aging populations remains fragmented and lacks systematic synthesis. This scoping review aimed to examine the literature on humanoid robot-assisted support for health care in older adults and identify gaps in the literature to guide future research. The methodological framework by Arksey and O'Malley was used to conduct this scoping review. We conducted a comprehensive search in 8 databases, including IEEE Xplore Digital Library, CINAHL, Cochrane Library, EMBASE, PubMed, Scopus, Web of Science, and OpenGrey Repository, covering literature published up to April 30, 2025. The reference lists of key texts were examined, and citation chaining was conducted. Two independent reviewers examined all full articles for fitness with the eligibility criteria and extracted data elements. The study findings were then summarized, coded, and analyzed using the PAGER (Patterns, Advances, Gaps, Evidence for practice, and Research recommendations) framework. A total of 32,477 articles were retrieved, 59 of which were included in this review. The majority (49/59, 83%) were conducted in real-world settings. Methodologically, 34 studies (34/59, 58%) had small sample sizes (n≤25), with study designs comprising 26 quantitative (26/59, 44%), 22 mixed method (22/59, 37%), and 11 qualitative (11/59, 19%) approaches. Participant characteristics revealed female predominance (>50%) in 32 studies (32/59, 54%), while 27 studies (27/59, 46%) included participants with cognitive impairment. Through PAGER framework analysis, we identified 4 key patterns: (1) effects, perceptions, and experiences of humanoid robots; (2) preferences, expectations, and facilitators for humanoid robots; (3) implementation barriers and challenges; and (4) determinants of user experiences and outcomes. This scoping review demonstrates the promising yet methodologically constrained potential of humanoid robots in health care for older adults while highlighting key challenges in their practical implementation. Successful integration will require addressing technical limitations, user acceptance barriers, and systemic adoption challenges.
Realistic reproduction of human facial expressions is essential for realistic interactions between humans and humanoid robots. This work presents a data-driven framework for transferring human facial expressions to a humanoid robot and a virtual avatar, aiming to enhance emotional expressiveness and assess its applicability in psychiatric training scenarios. The proposed approach enables cross-domain facial expression mapping while accounting for mechanical constraints of robotic actuation. A user study (n = 40) evaluated emotion recognition across three stimulus categories: human faces (H), unconstrained virtual avatars (A) and humanoid robots with limited facial actuation (R). Participants identified emotions from static images and from dynamic expression sequences, presented with and without speech. Perceived realism and uncanny valley effects were assessed using an eight-item questionnaire rated on a 7-point Likert scale. Results indicate that human-to-robot facial expression transfer is feasible but constrained by mechanical expressivity. Highly expressive emotions such as surprise (H: 87.5%; A: 57.5%; R: 65%) and fear (H: 45%; A: 27.5%; R: 57.5%) achieved moderate recognition rates, whereas subtle emotions such as anger (H: 65%; A: 40%; R: 12.5%) and disgust (H: 60%; A: 10%; R: 22.5%) were poorly recognized on the robot. Dynamic expressions combined with speech significantly improved recognition. These findings demonstrate the feasibility of transferring human facial expressions to humanoid robots while highlighting current limitations of robotic facial actuation. The proposed framework provides a promising basis for emotionally realistic patient simulation and training applications in mental healthcare.
Humanoid upper-limb robots are an important direction in biomimetic robotics, and inverse kinematics is a key technique for achieving human-like coordinated operation. However, existing inverse kinematics methods for bimanual trajectory tracking often suffer from high computational complexity and limited synchronization performance. To address this, this paper proposes an error-adaptive competition-based inverse kinematics (EAC-IK) approach for bimanual trajectory tracking of humanoid upper-limb robots. First, a unified modeling framework for the absolute tracking errors and synchronization errors of the two arms is established, and the end-effector task constraints are reformulated into a low-dimensional representation, thereby reducing the computational complexity of the original high-dimensional task mapping. Second, to enhance the coordination capability of bimanual operations, an error-adaptive competition mechanism is developed to regulate the weighting coefficients of the two arms online according to their error states. In addition, a virtual second-order command shaper is introduced at the joint level to reconstruct joint trajectories and suppress oscillations induced by input noise and the error-adaptive competition mechanism. Simulation and experimental results on a hyper-redundant humanoid upper-limb robot demonstrate that, compared with the zeroing neural-network-based inverse kinematics method, the proposed method achieves lower tracking and synchronization errors, as well as higher computational efficiency. In the circular trajectory-tracking experiment, the left-arm position and orientation tracking errors decrease from 1.60×10-3m and 4.72×10-3rad to 0.70×10-3m and 0.95×10-3rad, respectively, while the synchronization error decreases from 1.96×10-3 to 1.30×10-3. In addition, the average algorithm runtime decreases from 0.82ms to 0.63ms.
Socially assistive humanoid robots have emerged as promising tools for enhancing mood, engagement, and social connection in clinical and adult day care environments. This study investigated the acceptance and emotional impact of a humanoid robot deployed over an eight-week period in adult day care facilities, with the goal of reducing depressive affect and improving positive mood through activities such as trivia, karaoke, dancing, and humor. Sixty-five residents participated in the intervention. Affective and behavioral engagement were measured using the Robot Engagement Rating Scale (RERS), supplemented by qualitative field observations. The study assessed changes in engagement, social behaviors (e.g., eye contact), and participants' desire to continue interaction with the robot. RERS scores showed a significant increase in engagement, rising from 3.09 ± 0.27 at baseline to 4.61 ± 0.12 post-interaction, t(14) = 5.15, p < .001. Eye contact with the robot increased from 66.7% to 94.4% (p = .041), and the proportion of participants requesting to extend their interaction grew from 63.6% to 83.3%. Qualitative observations described laughter, verbal reciprocity, and sustained attention throughout the sessions, confirming positive emotional engagement. Conclusion / Discussion: Findings demonstrate that humanoid robots are highly acceptable and effective in eliciting positive emotional engagement among older adults in day care settings. These results underscore their therapeutic potential as complementary, socially enriching tools for promoting well-being and mood stabilization in long-term care and rehabilitation environments.
Humanoid robots can be seamlessly integrated into human-robot interaction scenarios due to their human-like appearances. Pneumatic artificial muscles (PAM) are promising actuators for such robots due to their similarity to biological muscles, but their limited contraction ratio constrains both appearance and motion range of the robot. This work presents a 7-degrees-of-freedom (DoF) pneumatic humanoid robotic arm that mimics the human arm in both appearance and movement capabilities. A hybrid actuation scheme, combining direct PAM actuation at shoulder joints and PAM actuation with Bowden cable transmission at the distal joints, is adopted to enable anthropomorphic scaling with a lightweight and compliant structure. To address the control challenges posed by the nonlinear dynamics of PAMs and Bowden cables, Pneumatic Bowden cable Optimized Soft Actor-Critic (PBO-SAC), a model-free reinforcement learning framework, is proposed to enable efficient on-hardware control policy learning for the robotic arm. PBO-SAC incorporates posture-perturbed decoupled training and local recurrent fusion networks to ensure safe and smooth policy learning. Simulation results verify improvements in PBO-SAC, while hardware experiments on trajectory tracking and teleoperated stacking tasks further demonstrate the multi-DoF coordination control performance.
Mechanical stimulation is essential in tissue engineering and regenerative medicine for proper tissue maturation. However, conventional uniaxial platforms fail to reproduce the multiaxial loading experienced in vivo. In this study, we present a humanoid robotic bioreactor capable of delivering human-like shoulder motions to engineered tendon constructs, enabling controlled multiaxial stimulation with real-time strain monitoring. Human mesenchymal stem cells were cultured on decellularized tendon scaffolds and subjected to adduction-abduction loading at peak strains of approximately 3.5% and 9.5% under external forces of 25 and 50 N, respectively. Strain levels were directly quantified in situ using a flexible sensor integrated within the bioreactor. The transparent bioreactor membrane allowed noninvasive observation while simultaneously applying mechanical stimulation over 14 d, with continuous assessment of cellular morphology without fixation. Compared with static and traditional uniaxial controls, the robot motions enhance cell alignment and activation of mechanotransduction pathways while inducing notable gene and protein expression changes, particularly within the PI3K-Akt signaling pathway. Although dynamic loading resulted in a moderate reduction in cell viability, the transcriptional profile was consistent with mechanically driven phenotypic adaptation toward tenogenic-related programs rather than dominant signatures of acute cytotoxic damage. These findings demonstrate that replicating human-like multiaxial mechanics in vitro fundamentally alters cellular mechanosensing and may provide a mechanobiological foundation for the future development of more physiologically relevant tendon grafts.
Drug discovery research faces significant challenges, with around 90 % of candidate drugs failing in clinical trials. Traditional animal models do not always accurately reflect human physiology and pathology, leading to low predictability of clinical outcomes. In vitro human model using induced pluripotent stem cells (iPSCs), offers a potential solution, but long culture periods and variability among experiments have prompted a shift towards automation in human biomimetic research. We optimized the automated cell culture system "Maholo LabDroid" to establish a platform for the long-term culture of undifferentiated iPSCs and for conducting complex cell-based assays. The Maholo LabDroid, a humanoid robotic system with excellent reproducibility, allows for the addition of peripherals and devices to execute new protocols. To enhance the use of iPSCs, we addressed limitations in detecting real-time changes in cells, supplying sufficient high-quality cells and handling 3D cell models that reflect more physiologically relevant conditions. We integrated a robot arm-assisted imaging system for real-time observation, introduced Bluetooth-enabled electronic pipettes for flexible liquid handling, and expanded the types of culture containers used. These improvements enabled us to determine optimal culture conditions, obtain sufficient quantities of cells, and automatically generate organoids with physiological functions. Using the LabDroid Maholo, we built a platform technology for the provision of advanced cellular models. This platform has the potential to enhance drug discovery research and contribute to the development of cell therapy products.
This study aims to enhance the precision of humanoid robots in imitating complex human "walking-grasping" coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control-guided by zero moment point (ZMP) and roll/pitch data-and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments.
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Acceptance of robots depends on whether they support consumers' psychological needs, making demand-side insights essential.
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At the 2025 International Conference of Intelligent Robots and Systems (IROS) in Zhejiang, China, leading researchers and industry experts debated whether humanoid robots will soon replace human workers. A summary of the points raised by the debaters for or against the claim is highlighted below.
Humanoid robots are being used more frequently in health care: in geriatrics and pediatrics, robots such as "Pepper" and "Nao" have been shown to enhance communication, emotional well-being, and patient engagement. During the pandemic, their role expanded, for example, to include remote monitoring and reducing the risk of infection. Given these applications, our study examines the integration of 2 advanced humanoid robots, Pepper and Nao, within the reception area of a geriatric hospital. The study aims to develop a reception service directed to enhance the quality of the IRCCS Istituto Nazionale di Riposo e Cura per Anziani (INRCA), Ancona, Italy. The primary end point of the study is the assessment of the perceived quality of an innovative reception service. Second, the study aims at evaluating the usability, human-robot interaction, user experience, and technical performance of the solution. The HOSPER (analysis of the quality of reception services using robotic platforms, Nao and Pepper) study is a feasibility pilot project coordinated by IRCCS INRCA. During the trial, 200 older adults using services at IRCCS INRCA in Ancona will be enrolled. Two humanoid robots, Nao and Pepper, which are already available at the hospital, will be used to support reception and orientation services. Data collection will include general demographics, cognitive and functional status, usability measures, perceived service quality, and human-robot interaction indicators. The recruitment and data collection started in January 2025 and will be concluded by December 2025. Results regarding the feasibility of a robotic-based reception service in a geriatric hospital will be published in 2027. The HOSPER study aims to assess the feasibility of an innovative robot-mediated reception service in a geriatric hospital, with specific focus on recruitment feasibility, completion rates, user acceptability, and perceived service quality. In fact, the usability and user experience evaluation will demonstrate the feasibility of the approach and provide indications for future developments and improvements.
As AI-generated characters become increasingly present in everyday emotional and romantic contexts, questions arise about whether they may begin to occupy relational space traditionally reserved for human partners. This study examines how character style (operationalized as holistic character conditions, each combining a distinct visual rendering, voice, and character background settings) and participant gender shape initial romantic evaluations of AI-generated and real human targets across four relational dimensions: Trust, Intimacy, Passion, and Commitment. A 4 (Character Style: 2D Anime, 3D Cartoon, Highly Humanoid, Real Human) × 2 (Participant Gender: Female, Male) mixed-design experiment was conducted with 134 Generation Z participants (72 female, 62 male; mean age = 19.91). Each participant viewed 30-s multimodal video introductions of four opposite-sex targets and rated them on a 12-item instrument adapted from automation trust scales and Sternberg's Triangular Love Scale. The results reveal two distinct patterns. On the foundational relational dimensions of Trust and Commitment, real human targets were generally rated higher than AI-generated conditions. For Trust, the real human target was rated significantly higher than all three AI conditions. For Commitment, this advantage was significant relative to the 3D Cartoon and Highly Humanoid conditions, but not relative to the 2D Anime condition. No significant Character Style × Gender interaction was found for either dimension, supporting the view that AI characters do not readily approach the relational standing of real humans on these judgments under brief initial exposure. On the affective engagement dimensions of Intimacy and Passion, significant Character Style × Gender interactions emerged: female participants reported elevated intimacy specifically toward 2D Anime targets, while male participants reported elevated passion specifically toward Highly Humanoid targets. These findings suggest that the relational boundary between AI-generated and real human targets is dimension-specific rather than absolute - firmly held on trust, largely maintained on commitment, but more permeable on intimacy and passion. The study contributes a cross-disciplinary measurement framework that integrates HCI trust assessment with relationship psychology, and reveals a pattern of dimensional separation in which affective engagement dimensions can shift independently of foundational relational dimensions.
Humanoid robots are being introduced in places where people do not speak the same language and people expect quick, natural responses. In such situations, speech interaction cannot afford noticeable delays. Most of the present speech-to-text systems are mainly maintained with cloud servers, leading to latency problems, reliance on dependable connectivity, and failures on the fly when used in real time. These shortcomings become especially evident when robots are expected to autonomously and continuously interact with human users. To address these limitations, this project proposes a new edge-centric speech-to-text framework tailored specifically for the multilingual humanoid robot domain. Instead of sending audio data to the cloud, this method performs speech processing directly within the robot. This technology includes lightweight neural models for real-time streaming, an onboard mechanism that allows for real-time identification of the target language, and local caching methods for quicker retrieval of repeated or known speech patterns. Combine these and you can get quicker, more trustworthy transcription without burning a hole in network resources. The system reduces communication delays to a great extent, while providing transcribed data in multiple languages due to local handling of speech from the wireless edge network. The time taken in overall response is more than 60% lower than the response time used in cloud-based systems; it has been found in experiments. More critically, the framework does a good job with fluctuating network bandwidth, loss of packets, and background noise. It is concluded that edge-based and multilingual speech-to-text systems will be important for humanoid robots to enhance responsivity and contextuality. Understanding faster results in faster reactions, smoother conversations, and moments of interaction that feel more natural is a major step toward pragmatic and reliable communication between humans and robots in the working world.
Advances in robotics are transforming health sciences education and helping to prepare students for future careers. Understanding students' perceptions, comfort, and attitudes towards these tools is important to help enhance learning. Using the JBI scoping review protocol and the Arksey and O'Malley framework, we conducted a comprehensive keyword search of the literature in CINAHL, ERIC, Education Source, MEDLINE, PsycINFO, and Web of Science, resulting in 576 unique records. Articles with a health care/health sciences student population, a physical robot, and an assessment of student attitudes were included. Eighteen studies encompassing a diverse group of participants, including nursing students, medical students, and multidisciplinary students in various fields such as communication, computer science, nutrition, and psychology, were included in this review. Robot types studied across these studies included: telepresence robots, surgical robots, humanoid robots, nonhumanoid robots, robot animal companions, and other robot classifications (social, service, and companion). Students consistently reported positive perceptions with increased satisfaction with robots, valuing their benefits for learning, stress reduction, and career enhancement interest. Further investigation of how students from various disciplines use educational robots and factors influencing their engagement will allow educators to tailor educational robotics and better serve our diverse student populations.
Humanoid robots and human-machine interaction technologies are essential for perceiving and manipulating millimeter-scale objects with irregular surfaces in extreme environments, such as outer space, radioactive zones, and hazardous sites with explosive ordnance, where human access is restricted. A vision-based perception approach provides spatial and positional information about objects but relying solely on it for robot manipulation poses challenges due to limitations in detectable object size, as well as sensitivity to external factors such as focusing issues, occlusion, and lighting conditions. In contrast, tactile perception offers valuable information about aspects that are difficult to discern visually, including an object's shape, surface characteristics, and the forces involved during contact. This study presents a complementary visual localization and tactile mapping framework that allows robots to effectively perceive small objects with irregular surfaces in visually restricted environments. The proposed method draws inspiration from the sequential vision-tactile sensory processing observed in humans when handling small objects with irregular surfaces. It employs an RGB-Depth camera for visual perception and a soft pressure sensor array, made using inkjet printing, for tactile perception. We demonstrate the feasibility of implementing a sensory substitution to detect the size and location of objects through visual perception, as well as identify object surfaces and reconstruct their three-dimensional profiles using tactile scanning, particularly in environments where visual information is limited. This study provides a technological foundation for enhancing the autonomy and adaptability of humanoid robots in unpredictable and unstructured environments, particularly to support precise robot manipulation in such conditions.