Accurate event detection is key to the successful design of semi-passive and powered prosthetics. Kinematically, the natural knee is complex, with translation and rotation components that have a substantial impact on gait characteristics. When simplified to a pin joint, some of this behaviour is lost. This study investigates the role of cruciate ligament stretch in event detection. A bicondylar knee design was used, constrained by analogues of the anterior and posterior cruciate ligaments. This offers the ability to characterize knee kinematics by the stretch of the ligaments. The ligament stretch was recorded using LVDTs parallel to the ligaments of the Russell knee on a bent knee crutch. Which was used to capture data on a treadmill at 3 speeds. This study finds speed dependence within the stretch of the cruciate ligaments, prominently around 5\% and 80\% of the gait cycle for the posterior and anterior. The cycle profile remains consistent with speed; therefore, other static events such as the turning point feature at around 90\% and 95\% of the cycle, for the posterior and anterior, respectively, could be used as a predictive precursor for initial contact. Likewise at 90\% and 95\%, another pair of turning points that in this case could be used to predict foot flat. This concludes that the use of a bicondylar knee design could improve the detection of events during the gait cycle, and therefore could increase the accuracy of subsequent controllers for powered prosthetics.

Following a stroke, individuals often experience mobility and balance impairments due to lower-limb weakness and loss of independent joint control. Gait recovery is a key goal of rehabilitation, traditionally achieved through high-intensity therapist-led training. However, manual assistance can be physically demanding and limits the therapist's ability to interact with multiple joints simultaneously. Robotic exoskeletons offer multi-joint support, reduce therapist strain, and provide objective feedback, but current control strategies often limit therapist involvement and adaptability. We present a novel gait rehabilitation paradigm based on physical Human-Robot-Human Interaction (pHRHI), where both the therapist and the post-stroke individual wear lower-limb exoskeletons virtually connected at the hips and knees via spring-damper elements. This enables bidirectional interaction, allowing the therapist to guide movement and receive haptic feedback. In a study with eight chronic stroke patients, pHRHI training outperformed conventional therapist-guided treadmill walking, leading to increased joint range of motion, step metrics, muscle activation, and motivation. These results highlight pHRHI's potential to combine robotic precision with therapist intuition for improved rehabilitation outcomes.

Enhancing the explosive power output of the knee joints is critical for improving the agility and obstacle-crossing capabilities of humanoid robots. However, a mismatch between the knee-to-center-of-mass (CoM) transmission ratio and jumping demands, coupled with motor performance degradation at high speeds, restricts the duration of high-power output and limits jump performance. To address these problems, this paper introduces a novel knee joint design paradigm employing a dynamically decreasing reduction ratio for explosive output during jump. Analysis of motor output characteristics and knee kinematics during jumping inspired a coupling strategy in which the reduction ratio gradually decreases as the joint extends. A high initial ratio rapidly increases torque at jump initiation, while its gradual reduction minimizes motor speed increments and power losses, thereby maintaining sustained high-power output. A compact and efficient linear actuator-driven guide-rod mechanism realizes this coupling strategy, supported by parameter optimization guided by explosive jump control strategies. Experimental validation demonstrated a 63 cm vertical jump on a single-joint platform (a theoretical improvement of 28.1\% over the optimal fixed-ratio joints). Integrated into a humanoid robot, the proposed design enabled a 1.1 m long jump, a 0.5 m vertical jump, and a 0.5 m box jump.

-- This paper presents a 3-DOF hopping robot with a human-like lower-limb joint configuration and a flat foot, capable of performing dynamic and repetitive jumping motions. T o achieve both high torque output and a large hollow shaft diameter for efficient cable routing, a compact 3K compound planetary gearbox was designed using mixed-integer nonlinear programming for gear tooth optimization. T o meet performance requirements within the constrained joint geometry, all major components--including the actuator, motor driver, and communication interface--were custom-designed. The robot weighs 12.45 kg, including a dummy mass, and measures 840 mm in length when the knee joint is fully extended. A reinforcement learning-based controller was employed, and the robot's performance was validated through hardware experiments, demonstrating stable and repetitive hopping motions in response to user inputs. These experimental results indicate that the platform serves as a solid foundation for future bipedal robot development. A supplementary video is available at: https://youtu.be/BZ2H0dQBcXc

To reproduce natural standing-up motion, recent studies have emphasized the importance of coordination between the assisting robot and the human. However, many non-wearable assistive devices have struggled to replicate natural motion trajectories. While wearable devices offer better coordination with the human body, they present challenges in completely isolating mechanical and electrical hazards. To address this, we developed a novel standing-assist robot that integrates features of both wearable and non-wearable systems, aiming to achieve high coordination while maintaining safety. The device employs a four-link mechanism aligned with the human joint structure, designed to reproduce the S-shaped trajectory of the hip and the arc trajectory of the knee during natural standing-up motion. Subject-specific trajectory data were obtained using a gyroscope, and the link lengths were determined to drive the seat along the optimal path. A feedforward speed control using a stepping motor was implemented, and the reproducibility of the trajectory was evaluated based on the geometric constraints of the mechanism. A load-bearing experiment with weights fixed to the seat was conducted to assess the trajectory accuracy under different conditions. Results showed that the reproduction errors for the hip and knee trajectories remained within approximately 4 percent of the seat's total displacement, demonstrating high fidelity to the target paths. In addition, durability testing, thermal safety evaluation, and risk assessment confirmed the reliability and safety of the system for indoor use. These findings suggest that the proposed design offers a promising approach for developing assistive technologies that adapt to individual physical characteristics, with potential applications in elderly care and rehabilitation.

This paper introduces a chain-driven, sandwich-legged, mid-size quadruped robot designed as an accessible research platform. The design prioritizes enhanced locomotion capabilities, improved reliability and safety of the actuation system, and simplified, cost-effective manufacturing processes. Locomotion performance is optimized through a sandwiched leg design and a dual-motor configuration, reducing leg inertia for agile movements. Reliability and safety are achieved by integrating robust cable strain reliefs, efficient heat sinks for motor thermal management, and mechanical limits to restrict leg motion. Simplified design considerations include a quasi-direct drive (QDD) actuator and the adoption of low-cost fabrication techniques, such as laser cutting and 3D printing, to minimize cost and ensure rapid prototyping. The robot weighs approximately 25 kg and is developed at a cost under \$8000, making it a scalable and affordable solution for robotics research. Experimental validations demonstrate the platform's capability to execute trot and crawl gaits on flat terrain and slopes, highlighting its potential as a versatile and reliable quadruped research platform.

Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, being one of the most disabling features in the progression of these diseases. Despite the potential benefit of using wearable robots to treat spasticity, their use is not currently recommended to subjects with a level of spasticity above ${1^+}$ on the Modified Ashworth Scale. The varying dynamics of this velocity-dependent tonic stretch reflex make it difficult to deploy safe personalized controllers. Here, we describe a novel adaptive torque controller via deep reinforcement learning (RL) for a knee exoskeleton under joint spasticity conditions, which accounts for task performance and interaction forces reduction. To train the RL agent, we developed a digital twin, including a musculoskeletal-exoskeleton system with joint misalignment and a differentiable spastic reflexes model for the muscles activation. Results for a simulated knee extension movement showed that the agent learns to control the exoskeleton for individuals with different levels of spasticity. The proposed controller was able to reduce maximum torques applied to the human joint under spastic conditions by an average of 10.6\% and decreases the root mean square until the settling time by 8.9\% compared to a conventional compliant controller.

In this work, we introduce the principle, design and mechatronics of Exo-Muscle, a novel assistive device for the knee joint. Different from the existing systems based on rigid exoskeleton structures or soft-tendon driven approaches, the proposed device leverages a new semi-rigid principle that explores the benefits of both rigid and soft systems. The use of a novel semi-rigid chain mechanism around the knee joint eliminates the presence of misalignment between the device and the knee joint center of rotation, while at the same time, it forms a well-defined route for the tendon. This results in more deterministic load compensation functionality compared to the fully soft systems. The proposed device can provide up to 38Nm assistive torque to the knee joint. In the experiment section, the device was successfully validated through a series of experiments demonstrating the capacity of the device to provide the target assistive functionality in the knee joint.

This paper presents a scalable method for friction identification in robots equipped with electric motors and high-ratio harmonic drives, utilizing Physics-Informed Neural Networks (PINN). This approach eliminates the need for dedicated setups and joint torque sensors by leveraging the robo\v{t}s intrinsic model and state data. We present a comprehensive pipeline that includes data acquisition, preprocessing, ground truth generation, and model identification. The effectiveness of the PINN-based friction identification is validated through extensive testing on two different joints of the humanoid robot ergoCub, comparing its performance against traditional static friction models like the Coulomb-viscous and Stribeck-Coulomb-viscous models. Integrating the identified PINN-based friction models into a two-layer torque control architecture enhances real-time friction compensation. The results demonstrate significant improvements in control performance and reductions in energy losses, highlighting the scalability and robustness of the proposed method, also for application across a large number of joints as in the case of humanoid robots.

Many studies have been conducted on Series Elastic Actuators (SEA) for robot joints because they are effective in terms of flexibility, safety, and energy efficiency. The ability of SEA to robustly handle unexpected disturbances has raised expectations for practical applications in environments where robots interact with humans. On the other hand, the development and commercialization of small robots for indoor entertainment applications is also actively underway, and it is thought that by using SEA in these robots, dynamic movements such as jumping and running can be realized. In this work, we developed a small and lightweight SEA using coil springs as elastic elements. By devising a method for fixing the coil spring, it is possible to absorb shock and perform highly accurate force measurement in both rotational directions with a simple structure. In addition, to verify the effectiveness of the developed SEA, we created a small single-legged robot with SEA implemented in the three joints of the hip, knee, and ankle, and we conducted a drop test. By adjusting the initial posture and control gain of each joint, we confirmed that flexible landing and continuous hopping are possible with simple PD position control. The measurement results showed that SEA is effective in terms of shock absorption and energy reuse. This work was performed for research purposes only.