Search Results (378)

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

This study examines first responder training to deliver realistic, adaptable, and scalable solutions aimed at equipping personnel to handle high-risk, rapidly developing scenarios. The proposed method leverages Virtual Reality, Augmented Reality, and digital twins to enable immersive and situationally relevant training for security-critical incidents. The method is structured into three distinct phases: definition, digitization, and implementation. The outcome of this approach is the creation of virtual training scenarios that simulate real situations and incident dynamics. The methodology employs photogrammetric reconstruction, simulation of human behavior through locomotion, and virtual security systems, such as surveillance and drone technology. Alongside the methodology, a case study of a large public event is presented to illustrate its feasibility in real-world applications. This study offers a comprehensive and adaptive structure for the design and deployment of digitally augmented training systems. This provides a practical basis for enhancing readiness in a range of operational domains. Full article

Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors. Full article

Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis. Full article

Achieving energy-efficient, human-like gait remains a major challenge in bipedal humanoid robotics, as traditional serial actuation architectures often lead to high instantaneous power peaks and uneven load distribution. This study addresses the lack of research on how mechanical symmetry, achieved through parallel actuation, can improve power management in lower-limb joints. We developed a 14-degree-of-freedom (DOF) hip-sized bipedal robot model and conducted simulations comparing a conventional serial configuration—using single-DOF rotary actuators—with a novel parallel configuration that employs paired linear actuators at the hip pitch, hip roll, ankle pitch, and ankle roll joints. Simulation results over a standardized walking cycle show that the parallel configuration reduces peak hip-pitch power by 80.4% and peak ankle-pitch power by 53.5%. These findings demonstrate that incorporating actuator symmetry through parallel joint design significantly reduces actuator stress, improves load sharing, and enhances overall energy efficiency in bipedal locomotion. Full article

This study aimed to evaluate how wearable hip exoskeleton assistance affects phase-dependent gait stability in healthy adults using a novel visualization technique known as gait tube analysis. Hip exoskeletons offer significant potential to enhance human locomotion through joint torque augmentation, yet their effects on gait stability across the gait cycle remain underexplored. This study introduces gait tube analysis, a novel method for visualizing center of mass velocity trajectories in three-dimensional state space, to quantify phase-dependent gait stability under hip exoskeleton assistance. We analyzed data from ten healthy adults walking under twelve conditions (ten powered with varying torque magnitude and timing, one passive, and one unassisted), assessing variability via covariance-based ellipsoid volumes. Powered conditions, notably HighLater and HighLatest, significantly increased vertical variability (VT) during early-to-mid stance (10–50% of the gait cycle), with HighLater showing the highest mean ellipsoid volume (99,937 mm³/s³; z = 2.3). Conversely, the passive PowerOff condition exhibited the lowest variability (47,285 mm³/s³; z = –1.7) but higher metabolic cost, highlighting a stability-efficiency trade-off. VT was elevated in 11 of 12 conditions (p ≤ 0.0059), and strong correlations (r ≥ 0.65) between ellipsoid volume and total variability validated the method’s robustness. These findings reveal phase-specific stability challenges and metabolic cost variations induced by exoskeleton assistance, providing a foundation for designing adaptive controllers to balance stability and efficiency in rehabilitation and performance enhancement contexts. Full article

5F-ADB, MDMB-4en-PINACA and ADB-4en-PINACA are three potent indazole-carboxamide synthetic cannabinoids (SCs) that have been widely abused in recent years. However, the pharmacological research on these compounds remains limited, especially in vivo research data. The purpose of the present study was to investigate the pharmacological effects of 5F-ADB, MDMB-4en-PINACA and ADB-4en-PINACA in mice, comparing their in vivo effects with those caused by Δ⁹-tetrahydrocannabinol (Δ⁹-THC), the main psychoactive substance in cannabis. We evaluated the cannabinoid-specific pharmacological effects of 5F-ADB, MDMB-4en-PINACA and ADB-4en-PINACA using the tetrad assay (locomotion inhibition, hypothermia, analgesia and catalepsy). Then we conducted conditioned place preference (CPP) and precipitated withdrawal assay to assess the rewarding effect and physical dependence, with Δ⁹-THC as a positive control. The results showed that all of the three SCs exhibited potential tetrad effects in a dose-dependent manner, with median effective dose (ED₅₀) values ranging from 0.03 to 0.77 mg/kg. In the CPP tests, they all exhibited a significant biphasic effect of conditioned place preference (CPP) and conditioned place aversion (CPA). A significant increase in paw tremors and head twitches was observed in the rimonabant-precipitated withdrawal assay, indicating that the repeated administration of these SCs can lead to potential physical dependence. All effective doses were lower than Δ⁹-THC. These findings strongly suggested that the three SCs exhibited similar but stronger cannabinoid-specific tetrad effects, rewarding effect and physical dependence compared with Δ⁹-THC, indicating their high abuse potential and possible threats to human health. The rank order of abuse potential for these drugs was 5F-ADB > MDMB-4en-PINACA > ADB-4en-PINACA > Δ⁹-THC. Full article

The continuous and reliable operation of autonomous systems is important for farm management decision making, whether such systems perform crop monitoring using imaging systems or crop handling in pruning and harvesting applications using robotic manipulators. Autonomous systems, including robotic ground vehicles, drones, and tractors, are major research efforts of precision crop management. However, these systems may be less effective or require specific customizations for planting systems in low tunnels, high tunnels, or other environmentally controlled enclosures. In this work, a compact and lightweight crop management platform is developed that uses overhead power cables for continuous operation over row crops, requiring less human intervention and independent of the ground terrain conditions. The platform does not carry batteries onboard for its operation, but rather pulls power from overhead cables, which it also uses to navigate over crop rows. It is developed to be modular, with the top section consisting of mobility and power delivery and the bottom section addressing a custom task, such as incorporating additional sensors for crop monitoring or manipulators for crop handling. This prototype illustrates the infrastructure, locomotive mechanism, and sample usage of the system (crop imaging) in the application of low-tunnel-covered strawberries; however, there is potential for other row crop systems with regularly spaced support structures to adopt this platform as well. Full article

Forestry is recognized as one of the most physically demanding professions. Walking in presents unique biomechanical challenges due to complex, irregular terrain, with several possible risks. This study investigated how human gait adapts across solid surfaces, forest trails, and natural forest environments. Fifteen healthy adult participants (average age 38.3; ten males and five females) completed 150 walking trials, with full-body motion captured via a 17 Inertial Measurement Unit (IMU) sensors (Xsens MVN Awinda system). The analysis focused on spatial and temporal gait parameters, including cadence, step length, foot strike pattern, and center of mass variability. Statistical methods (ANOVA and Kruskal–Wallis) revealed that surface type significantly influenced gait mechanics. On forest terrain, participants exhibited wider steps, reduced cadence, increased step and stride variability, and a substantial shift from heel-to-toe strikes. Gait adaptations reflect compensatory neuromuscular strategies to maintain body balance. The findings confirm that forestry terrain complexity compromises human gait stability and increases physical demands, supporting step variability and slip, trip, and fall risk. By identifying key biomechanical markers of instability, this study contributes to understanding human locomotion principles. Understanding these changes can help design safety measures for outdoor professions, particularly forestry. Full article

As manufacturing transitions into the era of Industry 5.0, the demand for systems that are not only intelligent but also human-centric, resilient, and sustainable is becoming increasingly critical. This paper introduces the Human and Humanoid-in-the-Loop (HHitL) ecosystem, a novel framework that integrates both humans and humanoid robots as collaborative agents within cyber–physical manufacturing environments. Building on the foundational principles of Industry 5.0, the paper presents a 6P architecture that includes participation, purpose, preservation, physical assets, persistence, and projection. The core features of this ecosystem, including anthropomorphism, perceptual intelligence, cognitive adaptability, and dexterity/locomotion, are identified, and their enablers are also introduced. This work presents a forward-looking vision for next-generation manufacturing ecosystems where human values and robotic capabilities converge to form adaptive, ethical, and high-performance systems. Full article

Background/objectives: Early childhood is marked by rapid physical and cognitive development, underscoring the critical influence of parental choices in shaping habits, pastimes, and traditions. The World Health Organization highlights habits of physical activity, sleep, and screen use as modulators of healthy development and well-being in young children. As a first, this birth cohort study investigates the relationship between movement behaviors in toddlerhood and subsequent sport participation during middle childhood. Methods: Participants are 930 boys and 926 girls from the Quebec Longitudinal Study of Child Development. Using a population-based analytical approach, we examined the relationship between parent-reported active leisure, sleep, and sedentary screen time at age 2 years and trajectory of sport participation between ages 6 and 10 years using sex-stratified linear regressions that account for a range of pre-existing individual and family confounding factors. Results: Many child and family characteristics predicted early childhood movement behaviors, distinctly for boys and girls. Moreover, parental regulation of screen time during early childhood significantly influenced their decisions to enroll and encourage sport participation in sons during school age. Specifically, boys who spent more than 1 h/day on screens had 10% fewer chances of consistently participating in sport throughout middle childhood, even after accounting for adherence to other lifestyle habits. Conclusions: Our findings suggest that parental values regarding early childhood screen exposure are an effective potential target for intervention fostering healthy and active development. Future research should focus on parent and child motivations for sport involvement in the long-term. Full article

Background/Objectives: Visuomotor integration relies on synchronized proprioceptive and visual feedback during visually guided locomotion. How the human brain processes unimodal or asynchronous multimodal inputs during locomotion is unclear. Methods: Using high-density mobile electroencephalography (EEG) and motion capture in a virtual reality environment, we investigated electrocortical responses during altered treadmill gait speeds (0.5 and 1.5 m/s) and visual flow speeds (0.5×, 1×, and 1.5× gait speed) among 13 healthy human subjects. Experimental conditions included passive viewing of a moving virtual environment, walking in a stationary virtual environment, and walking in a moving environment with synchronous and asynchronous visual flow. Results: At faster gait speed, we identified reduced premotor, sensorimotor, and visual electrocortical beta-band spectral power (13–30 Hz) and greater premotor cortex theta power (4–8 Hz). At faster visual flow speeds, we identified reduced sensorimotor electrocortical beta-band spectral power, reduced alpha (8–13 Hz) and beta power, and greater gamma-band power (30–50 Hz) from the visual cortex. During visual flow and gait speed mismatches, sensorimotor and parietal alpha- and beta-band electrocortical spectral power decreased at faster gait speed. During treadmill walking at 1.5 m/s, parietal electrocortical spectral power increased when visual flow exceeded gait speed. Conclusions: Electrical brain dynamics during human gait identified distinct neural circuits for integrating kinesthetic and visual information during visuomotor conflicts, gated by the parietal cortex. Full article

Backpacks are widely used as an efficient and convenient means for manual load transportation. However, carrying heavy loads for a long time can significantly increase the risk of health issues. In response to the growing demand for relieving muscle fatigue, this paper proposes an energy-saving backpack that can adapt effectively to variable walking speeds and load masses. Inspired by the traditional bamboo pole commonly used for transporting goods, an energy-saving theory based on its mechanical characteristics is proposed. Guided by the theory, the backpack is designed with adjustable stiffness to enhance adaptability across different usage scenarios. Under the experimental conditions of a load of 12 kg and variable walking speeds, the backpack achieves a minimum reduction of 8.6% in the root mean square (RMS) value of gastrocnemius muscle activation. Furthermore, when the load increases from 9 kg to 12 kg, the net metabolic rate is reduced by an average of at least 14.3% compared to conventional backpacks. The experimental results confirm the effectiveness of the proposed backpack under variable conditions, demonstrating the high adaptability and flexibility that the energy-saving backpack provides. Full article

The human pelvis is adapted to accommodate bipedal locomotion while retaining a wide enough pelvic canal to birth large babies. Many forces act on the pubic bone, with the pelvis being in charge of supporting the organs of the abdominopelvic cavity. In this research, we investigate whether increases in gut volume (GV) and number of births (parity) impact the skeletal morphology of the pubic bone at two regions: the pubic symphysis and the pubic arch. Our results indicate that in our female sample, the pubic symphysis width increased with increased GV and parity, while the pubic arch width decreased with increased GV and parity, although not significantly. In the male sample, there was almost no effect of increased GV on the pubic symphysis, while the pubic arch width increased in response to increased GV. We hypothesize that while significance is not present for this entire data set, these pelvic structures are impacted by GV and parity, and these changes should be investigated further. These changes in the structure can impact the function of the pelvic girdle and result in pain and changes to mobility. Pelvic girdle pain may be one result of these structural changes due to increased forces, and thus it is vital to investigate what factors may or may not contribute to these bone morphology changes. Full article

Background: Gravity profoundly influences human locomotion. Studies examining the effects of hyper-gravity on gait have largely relied on added external mass, potentially confounding results with changes in inertia and center of mass. This study attempted to isolate the effects of increased gravitational load on kinematics and electromyography during walking at several different levels of load. Methods: Fifteen healthy adults were exposed to simulated gravitational loads ranging from 100% to 130% of body weight using a novel harness and spring-based system that increased weight without the addition of external mass and without altering limb inertia. Participants walked on a treadmill at a self-selected speed through incremental loading and unloading. Lower limb kinematics and electromyography data were recorded. Traditional measures of gait, as well as more dynamical measures, including angle–angle analysis and phase portraits, were examined. Results: Data demonstrated that a 130% load is sufficient to induce kinematic changes at the hip and knee; however, these changes become significant only during the transition from 130% to lower load levels. Ankle kinematics and electromyography appeared to be unaffected. Conclusions: These findings suggest that the presence of external mass and alterations in limb inertias should be considered seriously as independent variables in future loading studies, and that weight and mass may need to be considered as separate effectors during locomotion. This study also found that the act of loading and unloading elicit distinct responses in the joints of the lower extremities, as well as that it may induce an adaptative after-effect. Full article