Wearable robotics are moving from research labs into everyday life as powered shoes and exoskeletons become consumer products designed to support natural movement.

WIRED put the latest consumer exoskeletons from Dnsys and Hypershell in a head-to-head test on a pro athletic track. Personal exoskeletons were everywhere at CES 2026 . There were ambitious designs from newcomers WiRobotics, Sumbu, Ascentiz, and Dephy, while Skip Mo/Go was back promoting its long-overdue tech trousers. Dnsys (pronounced Deen-sis), a comparatively well established name, had some new launches to tease, Hypershell was back with its top model, and Ascentiz had us sprinting across the show floor . An exoskeleton is a relatively new class of wearable device designed to enhance, support, or assist human movement, strength, posture, or even physical activity.

Although many roboticists today turn to nature to inspire their designs, even bioinspired robots are usually fabricated from non-biological materials like metal, plastic and composites. But a new experimental robotic manipulator from the Computational Robot Design and Fabrication Lab ( CREATE Lab) in EPFL's School of Engineering turns this trend on its head: its main feature is a pair of langoustine abdomen exoskeletons. Although it may look unusual, CREATE Lab head Josie Hughes explains that combining biological elements with synthetic components holds significant potential not only to enhance robotics, but also to support sustainable technology systems. "Exoskeletons combine mineralized shells with joint membranes, providing a balance of rigidity and flexibility that allows their segments to move independently. These features enable crustaceans' rapid, high-torque movements in water, but they can also be very useful for robotics. And by repurposing food waste, we propose a sustainable cyclic design process in which materials can be recycled and adapted for new tasks."

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Neurorehabilitation conventionally relies on the interaction between a patient and a physical therapist. Robotic systems can improve and enrich the physical feedback provided to patients after neurological injury, but they under-utilize the adaptability and clinical expertise of trained therapists. In this position paper, we advocate for a novel approach that integrates the therapist's clinical expertise and nuanced decision-making with the strength, accuracy, and repeatability of robotics: Robot-mediated physical Human-Human Interaction. This framework, which enables two individuals to physically interact through robotic devices, has been studied across diverse research groups and has recently emerged as a promising link between conventional manual therapy and rehabilitation robotics, harmonizing the strengths of both approaches. This paper presents the rationale of a multidisciplinary team-including engineers, doctors, and physical therapists-for conducting research that utilizes: a unified taxonomy to describe robot-mediated rehabilitation, a framework of interaction based on social psychology, and a technological approach that makes robotic systems seamless facilitators of natural human-human interaction.

Hand exoskeletons are critical tools for dexterous teleoperation and immersive manipulation interfaces, but achieving accurate hand tracking remains a challenge due to user-specific anatomical variability and donning inconsistencies. These issues lead to kinematic misalignments that degrade tracking performance and limit applicability in precision tasks. We propose a subject-specific calibration framework for exoskeleton-based hand tracking that estimates virtual link parameters through residual-weighted optimization. A data-driven approach is introduced to empirically tune cost function weights using motion capture ground truth, enabling accurate and consistent calibration across users. Implemented on the Maestro hand exoskeleton with seven healthy participants, the method achieved substantial reductions in joint and fingertip tracking errors across diverse hand geometries. Qualitative visualizations using a Unity-based virtual hand further demonstrate improved motion fidelity. The proposed framework generalizes to exoskeletons with closed-loop kinematics and minimal sensing, laying the foundation for high-fidelity teleoperation and robot learning applications.

In this paper, an approach for gait assistance with a lower body exoskeleton is described. Two concepts, transparency and motion assistance, are combined. The transparent mode, where the system is following the user's free motion with a minimum of perceived interaction forces, is realized by exploiting the gear backlash of the actuation units. During walking a superimposed assistance mode applies an additional torque guiding the legs to their estimated future position. The concept of adaptive oscillators is utilized to learn the quasi-periodic signals typical for locomotion. First experiments showed promising results.

Electromyography (EMG) signals are widely used for predicting body joint angles through machine learning (ML) and deep learni ng (DL) methods. However, these approaches often face challenges such as limited real - time applicability, non - representative test c onditions, and the need for large datasets to achieve optimal performance. This paper presents a transfer - learning framework for knee joint angle prediction that requires only a few gait cycles from new subjects. Three datasets - Georgia Tech, the Universi ty of California Irvine (UCI), and the Sharif Mechatronic Lab Exoskeleton (SMLE) - containing four EMG channels relevant to knee motion were utilized. A lightweight attention - based CNN - LSTM model was developed and pre - trained on the Georgia Tech dataset, t hen transferred to the UCI and SMLE datasets. The proposed model achieved Normalized Mean Absolute Errors (NMAE) of 6.8 percent and 13.7 percent for one - step and 50 - step predictions on abnormal subjects using EMG inputs alone. Incorporating historical knee angles reduced the NMAE to 3.1 percent and 3.5 percent for normal subjects, and to 2.8 percent and 7.5 percent for abnormal subjects. When f urther adapted to the SMLE exoskeleton with EMG, kinematic, and interaction force inputs, the model achieved 1.09 p ercent and 3.1 percent NMAE for one - and 50 - step predictions, respectively. These results demonstrate robust performance and strong generalization for both short - and long - term rehabilitation scenarios . Keywords: EMG, Transfer Learning, Knee Angle Prediction, Attention Mechanism, Rehabilitation, Exoskeleton . 1 - Introduction Electromyography (EMG) measures electrical signals generated by contracting muscle fibers, reflecting neuromuscular activity. EMG is typically measured using electrodes placed on the skin's surface (surface Electromyography (sEMG)). Alternatively, electrodes may be inserted into the muscle tissue [2] . The frequency range of EMG signals is generally reported to be from 6 to 500 Hz, with most power concentrated between 20 and 250 Hz [3] . Analyzing EMG signals provides valuable information about muscle activation patterns, coordination, and fatigue levels.

Figure 1: L T -Exosense is capable of merging multiple sessions generated by a previous work, Exosense, a vision-centric scene understanding system with its sensing unit (T op-Right) integrated into a self-balancing exoskeleton (b). The merged map (a) contains five sessions with colored contours indicating the coverage area of each session. Such a merged map can be further converted into a navigation map, enabling obstacle-free planning spanning multiple sessions. Abstract-- Self-balancing exoskeletons offer a promising mobility solution for individuals with lower-limb disabilities. For reliable long-term operation, these exoskeletons require a perception system that is effective in changing environments. In this work, we introduce L T -Exosense, a vision-centric, multi-session mapping system designed to support long-term (semi)- autonomous navigation for exoskeleton users. L T -Exosense extends single-session mapping capabilities by incrementally fusing spatial knowledge across multiple sessions, detecting environmental changes, and updating a persistent global map. This representation enables intelligent path planning, which can adapt to newly observed obstacles and can recover previous routes when obstructions are removed. We validate L T -Exosense through several real-world experiments, demonstrating a scalable multi-session map that achieves an average point-to-point error below 5 cm when compared to ground-truth laser scans.

This study presents a real-time, portable brain-computer interface (BCI) system designed to support hand rehabilitation for stroke patients. The system combines a low cost 3D-printed robotic exoskeleton with an embedded controller that converts brain signals into physical hand movements. EEG signals are recorded using a 14-channel Emotiv EPOC+ headset and processed through a supervised convolutional autoencoder (CAE) to extract meaningful latent features from single-trial data. The model is trained on publicly available EEG data from healthy individuals (WAY-EEG-GAL dataset), with electrode mapping adapted to match the Emotiv headset layout. Among several tested classifiers, Ada Boost achieved the highest accuracy (89.3%) and F1-score (0.89) in offline evaluations. The system was also tested in real time on five healthy subjects, achieving classification accuracies between 60% and 86%. The complete pipeline - EEG acquisition, signal processing, classification, and robotic control - is deployed on an NVIDIA Jetson Nano platform with a real-time graphical interface. These results demonstrate the system's potential as a low-cost, standalone solution for home-based neurorehabilitation.