Compliance control is important for the realization of disturbance absorption in biped robots. However, under a sustained disturbance, compliance control causes the robot’s balance to deteriorate because of its floating base nature. Humans address this problem by resisting external disturbance. When pushed, a human will reconcile their posture with the applied external force and then push back to maintain balance. Inspired by this behavior, we propose a compliance control strategy for biped robots called resistant compliance, which allows a robot to comply with the external disturbance initially and then repel the disturbance to reduce the imbalance caused by the reconciliatory motion. As a result, the robot can obtain improved environment-interaction stability and react more like a typical human, thus making both its locomotion and its interactions more stable and safer. To realize this control strategy, the virtual-mass-model (VMM) control is redesigned to unify the disturbances from unexpected external forces and an inclined floor. Then, the VMM control is combined with the linear-inverted-pendulum model to realize resistant compliant motion. Model predictive control is used to track the reference zero-moment-point trajectory, which is essential for locomotion. To validate the proposed control strategy, the method is implemented on the human-sized humanoid robot BHR-T.

Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. However, the features learned through deep learning are incomprehensible, and this issue has precluded the use of deep learning in clinical applications where model comprehension is required. In this paper, a generative flow model (GFM), which is a recent flourishing branch of deep learning, is used with a SoftMax classifier for hand-gesture classification. The proposed approach achieves $63.86\pm 5.12\%$ accuracy in classifying 53 different hand gestures from the NinaPro database 5. The distribution of all 53 hand gestures is modelled by the GFM, and each dimension of the feature learned by the GFM is comprehensible using the reverse flow of the GFM. Moreover, the feature appears to be related to muscle synergy to some extent.

The bi-articular muscle-tendon complex (MTC) in an animal's leg allows a transfer of mechanical power between adjacent joints and exertion of a large force at the toes. It is capable of storing and releasing elastic energy efficiently during motions. In our previous studies, we proposed mechanisms of the bi-articular MTC for achieving extension of the upper and lower parts of the leg and experimentally demonstrated that each mechanism allows the higher jumping motion of a legged robot. In this study, we developed a bio-inspired legged robot by integrating the upper and lower leg bi-articular MTCs and investigated the effect of their cooperative utilization on the improvement of the vertical jumping ability. A jumping motion trajectory was generated through optimization to maximize the jumping ability based on a dynamic model of the bio-inspired legged robot with these bi-articular MTCs. The simulation results of the models with and without bi-articular MTCs were compared. Finally, through jumping experiments performed by implementing the generated motion trajectory, it was confirmed that the jumping ability was improved by the cooperative utilization of those bi-articular MTCs.

In order to study the axial compression behavior of rectangular concrete short columns that have been strengthened with basalt fiber-reinforced polymer sheets, the axial compression test were conducted on 15 groups specimens. The parameters are the cross aspect ratio and the number of basalt fiber-reinforced polymer layers. As the aspect ratio increases, the confined effect of basalt fiber-reinforced polymer decreases. The strength of one ply of basalt fiber-reinforced polymer decreases as the aspect ratio increases, the range is not very obvious, but the two ply of basalt fiber-reinforced polymer has the obvious decreasing tendency. With the increase in aspect ratio, the square and rectangular section columns of basalt fiber-reinforced polymer confined have a certain size effect. When the aspect ratio is 1.0, the values of f_cc/f_c0 by one ply of basalt fiber-reinforced polymer and two ply of basalt fiber-reinforced polymer are 1.14 and 1.50, respectively. And when the ratio is 2.0, the values of f_cc/f_c0 are 1.06 and 1.16. The confined effect is best when the aspect ratio is 1.0, while there is no significant effect with the ratio of 2.0. Results of the theoretical analysis compared with the experimental results show good agreement.

Recently, the development of agile-legged robots that mimic the structures and functions of biological muscles and tendons of quadruped mammals has attracted significant attention. The driving force of muscles and the elastic force of muscles and tendons can help improve the performances of these robots. Thus far, most of these robots have been developed based on the muscle-tendon complex of the lower legs. However, some mammals have a larger muscle-tendon complex on the upper legs, which contributes significantly to agile movements. In this study, a legged robot that mimics the functions of the bi-articular muscle-tendon complex of the thigh was developed, and its effects on static and dynamic movements were verified through simulations and experiments. While maintaining a static posture, the transfer of torque between two joints in the thigh mechanism reduced the power consumption by the robot’s motors by half, used for balancing the gravity of the robot. For a dynamic vertical jump, the motion trajectory generation for the robot was performed using non-linear optimization to maximize the jump height. As a result, the jump height was significantly enhanced by the effect of the thigh mechanism, and the height was more than twice the leg length.

Toward swift running on biped robots, this article provides a complete design approach for a robot leg featuring reduced rotational inertia and introduces a novel mechanic optimization scheme to meet severe mechanical requirements. We adopt a ballscrew driving setup to design the inertia-reduced driving for the leg. When designing the actuator's transmission to satisfy the joints' speed and torque demands, it is challenging to specify the mechanism parameters because their effects are nonlinear and highly coupled. To address this problem, we invite a novel idea that uses target motion trajectories to guide the optimization process and make the mechanism fit the motion best. For that purpose, the joint space position is divided into a high-speed and high-torque region by analyzing the target joint trajectories. Then, the optimization scheme is formulated according to this division to obtain the optimized joint position–transmission ratio curve. The proposed technique can dramatically reduce the torque demands, making a good tradeoff in reconciling the dilemma between the joint speed and torque demands. Finally, the proposed scheme is validated with a 7 km/h running motion on the biped robot BHR-T, enhancing the robot with the desired athletic capacities.