Online gait planning plays a crucial role for the locomotion of humanoid robots. While simplified models often fail to capture critical dynamic features of the robot’s motion, making online gait modifications with constrained nonlinear optimization in complex models is highly challenging with current computational power. This paper introduces a gait planning method that leverages supervised learning to expedite the gait planning and optimization process. Building upon our previous work, this paper extends a three-body model to the three-dimensional (3D) case. This model incorporates the angular momentum and height variation of body as well as the influence of leg motions, thus facilitating the generation of omnidirectional walking and running patterns. Due to the complexity of the model, an online gait planning modification is impractical. Therefore, supervised-learning is employed to train a policy derived from the model-based gait planning approach. This policy is then implemented online to produce versatile locomotion. Furthermore, the gradient of the trained neural network is utilized for nonlinear optimization of gait parameters, significantly improving the robot’s balance against external perturbations. The effectiveness of the proposed method is validated through a series of experiments conducted in simulation and on the real robot BHR-T, confirming its capability to generate adaptive walking and running motions in response to varying demands and disturbances.

Unmanned aerial vehicles (UAVs) and infrared imaging technology have numerous applications in civilian fields. To address the issues of low accuracy resulting from complex ground backgrounds, small target size, and limited target features in UAV remote sensing infrared image target detection, we use the YOLOv9s model and the latest retentive networks meet vision transformers (RMTs) technology and propose the RMT-YOLOv9s model for infrared small target detection. First, a convolutional neural network (CNN)-RMT-based backbone is proposed by incorporating the RMT model into the backbone network of YOLOv9s, which extracts both local and global features for small target detection. Then, an improved neck multiscale feature-fusion network RMTELAN-PANet is designed using the novel convolutional RMTELAN module proposed in this letter, which can better capture and use semantic information from feature maps. Finally, efficient multiscale attention (EMA) attention module and upsampling Dysample module are integrated into RMTELAN-PANet to further improve the feature information of small targets. Experiments on the HIT-UAV dataset show that RMT-YOLOv9s outperforms other popular methods in infrared small target detection.

Highly dynamic movements such as jumping are important to improve the agility and environmental adaptation of humanoid robots. This article proposes an online optimization method to realize a vertical jump with centroidal angular momentum (CAM) control and landing impact absorption for a humanoid robot. First, the robot's center of mass (CoM) trajectory is generated by nonlinear optimization. Then, a quasi-sliding mode controller is designed to ensure that the robot tracks the CoM trajectory accurately. To avoid unexpected spinning in the flight phase, a center-of-pressure-guided angular momentum controller is designed to stabilize the CAM. The modifications of CoM and CAM are realized by online optimization of dynamic components and inverse dynamics. Two quadratic programming optimizations are utilized to generate feasible contact force/torque and joint acceleration referring to uplevel CoM and CAM controllers. In addition, a viscoelastic model-based controller is designed to absorb the vibration caused by a large contact impact. A simulation and experiment of a 0.5-m high (foot lifting distance) vertical jump are achieved on a humanoid robot platform in this article (Fig. 1).