The aging population is now a global challenge, and impaired walking ability is a common feature in the elderly. In addition, some occupations such as military and relief workers require extra physical help to perform tasks efficiently. Robotic hip exoskeletons can support ambulatory functions in the elderly and augment human performance in healthy people during normal walking and loaded walking by providing assistive torque. In this review, the current development of robotic hip exoskeletons is presented. In addition, the framework of actuation joints and the high-level control strategy (including the sensors and data collection, the way to recognize gait phase, the algorithms to generate the assist torque) are described. The exoskeleton prototypes proposed by researchers in recent years are organized to benefit the related fields realizing the limitations of the available robotic hip exoskeletons, therefore, this work tends to be an influential factor with a better understanding of the development and state-of-the-art technology.

Exoskeleton robots hold promising prospects for rehabilitation training in individuals with weakened muscular conditions. However, achieving improved human–machine interaction and delivering customized assistance remains a challenging task. This paper introduces a muscle synergy-based human-in-the-loop (HIL) optimization framework for hip exoskeletons to offer more personalized torque assistance. Initially, we propose a muscle synergy similarity index to quantify the similarity of synergy while walking with and without the assistance of an exoskeleton. By integrating surface electromyography (sEMG) signals to calculate metrics evaluating muscle synergy and iteratively optimizing assistance parameters in real time, a muscle synergy-based HIL optimized torque configuration is presented and tested on a portable hip exoskeleton. Iterative optimization explores the optimal and suboptimal assistance torque profiles for six healthy volunteers, simultaneously testing zero torque and predefined assistance configurations, and verified the corresponding muscle synergy similarity indices through experimental testing. In our validation experiments, the assistance parameters generated through HIL optimization significantly enhance muscle synergy similarity during walking with exoskeletal assistance, with an optimal average of 0.80 ± 0.04 (mean ± std), marking a 6.3% improvement over prior assistive studies and achieving 96.4% similarity compared with free walking. This demonstrates that the proposed muscle synergy-based HIL optimization can ensure robotic exoskeleton-assisted walking as “natural” as possible.

In recent years, "edge intelligent computing power deployment" has become a key and difficult problem in key areas such as system structure and artificial intelligence. Some independent decision-making scenario requires the edge devices to have the ability to handle high energy efficient and high throughput tasks. However, restricted by the traditional accurate computing mode and memory-computation separation structure, the edge devices often fail to provide the computing power guarantee of high throughput and high energy efficiency, which seriously hinders the edge intelligent deployment. This paper summarizes the new computing model integrating memory and computation of deploying stochastic circuits around memory, and constructs an edge intelligent system taking stochastic computing as a core from three levels of microstructure of circuit, architecture of near-memory stochastic computing and system-level software/hardware cooptimization. It strives to break through the power consumption and performant bottleneck of traditional computing model, and greatly improves the deploying ability of complex intelligent computing on edge equipment.

In legged robot state estimation without foot-contact measurements, nominal gait phase does not directly indicate the instantaneous validity of foot–ground kinematic constraints, while residual errors in state propagation and kinematic pseudo-measurements can further bias the state estimate. A contact-reliability-aware neural residual Kalman filtering framework is developed while retaining the physics-based state-space model and Kalman recursion. A multi-task temporal estimator infers gait-phase-constrained continuous contact reliability together with dynamic and observation residuals from proprioceptive history sequences. The estimated reliability regulates contact-related process and measurement covariances to adjust the confidence assigned to foot–ground kinematic constraints. The dynamic residual compensates base-velocity prediction errors, and the observation residual corrects predictable errors in foot kinematic pseudo-measurements through the measurement innovation. Simulation results show lower base-state and foot-position errors than the Binary Contact Kalman filter across multiple motion commands. Physical walking and turning experiments show improved horizontal base trajectory estimation and lower terminal drift within the prescribed onboard update period.