Abstract: A trajectory planning method, a computer-readable storage medium, and a robot are provided. The method includes: constructing a phase variable of a trajectory planning of a robot, where the phase variable is a function of two position components of a torso of the robot on a horizontal plane; and performing, using the phase variable replacing a time variable, the trajectory planning on a swinging leg of the robot in each preset coordinate axis direction. In this manner, the robot can no longer continue to follow the established trajectory after being disturbed by the environment, but make state adjustments according to the disturbance received to offset the impact of the disturbance, thereby maintaining walking stability and avoiding the problem of early or late landing of the swinging leg.

Abstract: A method of controlling a robot includes: obtaining an inertia matrix and a slack variable of the robot, and determining a momentum equation of the robot according to the inertia matrix and the slack variable; obtaining reference joint angles corresponding to a reference action of the robot; determining an optimization objective function of the momentum equation according to a first preset weight coefficient of the slack variable and a second preset weight coefficient of the reference joint angles; and determining joint angles of the robot according to the optimization objective function, and driving the robot to move according to the joint angles of the robot.

Abstract: A biped robot control methods and a biped robot using the same as well as a computer readable storage medium are provided. The method includes: obtaining an initial distance between a centroid of a double inverted pendulum model of the biped robot and a support point of the biped robot, an initial moving speed of the centroid and an initial displacement of the centroid; calculating a measured value of a stable point of the doable inverted pendulum model based on the initial distance and the initial moving speed; calculating a control output quantity based on the initial moving speed and the measured value of the stable point; calculating a desired displacement of the centroid of the double-inverted pendulum model based on the initial moving speed, the initial displacement, and the control output quantity; and controlling the biped robot to move laterally according to the desired displacement.

Abstract: A robot stability control method includes: obtaining a desired zero moment point (ZMP) and a fed-back actual ZMP of a robot at a current moment; based on a ZMP tracking control model, the desired ZMP and the actual ZMP, calculating a desired value of a motion state of a center of mass of the robot at the current moment, wherein the desired value of the motion state of the center of mass comprises a correction amount of the position of the center of mass; based on a spring-mass-damping-acceleration model and the desired value of the motion state of the center of mass, calculating a lead control input amount for the correction amount of the position of the center of mass; and controlling motion of the robot according to the lead control input amount and a planned value of the position of the center of mass at the current moment.

Abstract: A jumping motion control method for a biped robot includes: before feet of the biped robot leaves a support surface, estimating a motion trajectory of the biped robot that leaves the support surface according to a period of time when the biped robot stays or flips in the air; calculating a first motion angle of each joint of legs of the biped robot according to the motion trajectory and inverse kinematics; determining a constraint condition according to a motion type to which an action to be performed by the biped robot corresponds; optimizing the first motion angles according to the constraint condition to obtain a second motion angle of each joint of legs of the biped robot; and controlling a jumping motion of the biped robot according to the second motion angles.

Abstract: Provided are a method and apparatus for transmitting downlink control information. The method includes transmitting the downlink control information (DCI) through a physical downlink control channel (PDCCH); and scheduling multiple transport blocks (TBs) through the DCI. The scheduled multiple TBs are indicated by new data indicator (NDI) information and hybrid automatic repeat request (HARQ) process information in the DCI.

Abstract: Provided are an information receiving method, an information sending method, a communication node, and a storage medium. The information receiving method includes receiving (S110) first information sent by a second communication node; and determining (S120) a temporary ID of the second communication node according to a resource serial number of a transmission resource used for transmission of the first information. The temporary ID includes a bit sequence corresponding to the resource serial number. The resource serial number includes at least one of the following: a round serial number, a slot serial number, a group serial number, a spreading code serial number, or a subchannel serial number.

Abstract: Provided are a configuration method and apparatus, a receiving method and apparatus, a device, and a storage medium. The configuration method includes: determining a modulation and coding scheme (MCS) set based on a higher-layer configuration parameter; and configuring an MCS of data based on the MCS set; where the higher-layer configuration parameter indicates whether data transmission supports a 16 quadrature amplitude modulation (16QAM) modulation scheme, and the MCS set includes one or more of: a first MCS set and a second MCS set.

Abstract: A robot control method, a robot, and a computer-readable storage medium are provided. The method includes: obtaining a linear motion model of a robot; determining a predicted state corresponding to each moment in a preset time period based on the linear motion model; determining an expected state corresponding to each moment in the preset time period; and determining a compensation value of a velocity of joint(s) at each moment from k-th moment to k+N?1-th moment based on the predicted state corresponding to each moment in the preset time period and the expected state corresponding to each moment in the preset time period, determining instruction parameter(s) at the k-th moment based on the compensation value of the velocity of the joint(s) at the k-th moment, and adjusting a position of each of the joint(s) of the robot according to the instruction parameter(s) at the k-th moment.

Abstract: Methods, apparatus and systems for determining a transmission resource for transmitting a wake-up signal in a wireless communication are described. In one embodiment, a method performed by a wireless communication node is disclosed. The method comprises: determining an initial transmission resource for wake-up signal (WUS) transmission; determining an updated transmission resource for WUS transmission based at least on the initial transmission resource and an offset; and transmitting, on the updated transmission resource, a WUS to at least one wireless communication device, wherein the offset is selected from a plurality of predetermined values based on a location of a paging occasion (PO) corresponding to the WUS.

Abstract: A data transmission method and device, and a resource determination method and device, the method including user equipment (UE) transmitting a signal on one or more first uplink channel resources, where the signal includes one of the following: a single subcarrier signal, a multi-subcarrier signal, or a random access signal.

Abstract: A garment can include a first fabric panel and a second fabric panel joined to the first fabric panel, the second fabric panel comprising reflective thread, the reflective thread defining a grid pattern including a plurality of first threads parallel to each other and a plurality of second threads parallel to each other and angled with respect to each of the plurality of first threads.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: A pneumatic tire includes: bead cores formed by winding bead wires in a ring shape and having a polygonal cross-section shape in a meridian cross-section; a carcass extended between bead portions on both sides in a width direction, the carcass being folded back to an outer side in the width direction of the bead portion, the carcass being formed by coating a carcass cord with a coating rubber; an inner organic fiber reinforced layer wound around the bead core and formed by coating an organic fiber cord with a coating rubber; and a rubber reinforcing layer between the inner organic fiber reinforced layer and the carcass to cover at least an apex on an innermost side of the bead core in the width direction. A shortest distance between the bead wire and the carcass cord is 3 mm or more.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: A textual user input is received and a plurality of different text-to-content models are run on the textual user input. A selection system attempts to identify a suggested content item, based upon the outputs of the text-to-content models. The selection system first attempts to generate a completed suggestion based on outputs from a single text-to-content model. It then attempts to mix the outputs of the text-to-content models to obtain a completed content suggestion.