A feedforward control method comprising steps of: acquiring kinematic parameters of each joint of a robot based on inverse kinematics according to a pre-planned robot motion trajectory, and setting a center of a body of the robot as a floating base; determining a six-dimensional acceleration of a center of mass of each joint of the robot in a base coordinate system using a forward kinematics algorithm, based on the kinematic parameters of each joint of the robot, and converting the six-dimensional acceleration of the center of mass of each joint of the robot in the base coordinate system to a six-dimensional acceleration in a world coordinate system; and calculating a torque required by a motor of each joint of the robot using an inverse dynamic algorithm, and controlling the motors of corresponding joints of the robot.

This patent proposal document provides a complete robot hand control scheme using myoelectric intention estimation of the human being using the kernel Principal Component Analysis Algorithm (kPCA). The robot hand system includes a biometric EMG sensor system, a robot hand including with multiple fingers, a controller connected with the biometric EMG sensor system, and a robot hand. The controller acquires the biometric EMG signal by means of a biometric sensor system, estimates myoelectric motion intention by applying the kernel principal component analysis (kPCA) algorithm using a kernel function, and delivers a control command corresponding to the estimated motion intention of the user to the robot hand.

A robot (100) includes a resistance circuit (60) configured or programmed to perform a control to reduce a braking force of a dynamic brake by changing a resistance value of a resistance component (63) with respect to a power supply path (61) when motors (30) are stopped at an abnormal stop.

Disclosed are a body channel communication method and an apparatus performing the same. An operating method of a transmitter includes obtaining an input signal including biometric information, generating an encoded signal and a control signal by encoding the input signal, generating a return-to-zero (RZ) signal of a biphasic waveform based on the encoded signal, and transmitting the RZ signal through a body channel.

A computer-implemented gait planning method includes: determining a pitch angle between a foot of the robot and a support surface where the robot stands; determining a support point on a sole of the foot according to the pitch angle; calculating an ankle-foot position vector according to the support point, wherein the ankle-foot position vector is a position vector from an ankle of the robot to a support point on a sole of the foot; calculating a magnitude of change of an ankle position according to the pitch angle and the ankle-foot position vector; and obtaining a compensated ankle position by compensating the ankle position according to the magnitude of change of the ankle position.

Systems and methods for determining movement of a robot about an environment are provided. A computing system of the robot (i) receives information including a navigation target for the robot and a kinematic state of the robot; (ii) determines, based on the information and a trajectory target for the robot, a retargeted trajectory for the robot; (iii) determines, based on the retargeted trajectory, a centroidal trajectory for the robot and a kinematic trajectory for the robot consistent with the centroidal trajectory; and (iv) determines, based on the centroidal trajectory and the kinematic trajectory, a set of vectors having a vector for each of one or more joints of the robot.

Robots, methods, and computer program products for training and operating (semi-) autonomous robots to complete multiple different work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. A robot can be deployed with an appropriate stored library (or access to an appropriate library) of reusable work primitives, based on what the robot is expected to do, or what service category or role the robot will operate in.

A detection unit of a control device detects a state of hardness of a target work surface from a captured image, which includes the target work surface, by using data acquired by machine-learning a relationship between the captured image and the state of hardness of the target work surface. The target work surface is a surface upon which the to-be-operated device applies force. A plurality of control modes are set, in accordance with differences in the state of hardness of the target work surface, as control modes for controlling the action of the to-be-operated device in response to a command output from the operation equipment. On the basis of the command and the state of hardness of the target work surface as detected by the detection unit, a selection unit selects a control mode to execute.

A decoupling control method for a humanoid robot includes: decomposing tasks of the humanoid robot to obtain kinematic tasks and dynamic tasks, and classifying corresponding joints of the humanoid robot into kinematic task joints or dynamic task joints; solving desired positions and desired speeds of the kinematic task joints for performing the kinematic tasks according to desired positions and desired speeds of ends in the kinematic tasks using inverse kinematics; calculating torques of the kinematic task joints based on the desired positions and desired speeds of the kinematic task joints; and solving a pre-built optimization model of torques required for the dynamic task joints based on the calculated torques of the kinematic task joints, to obtain torques required by the dynamic task joints for performing the dynamic tasks.

A robot balance control method includes: obtaining force information associated with a left foot and a right foot of the robot; calculating a zero moment point of a center of mass (COM) of a body of the robot based on the force information; calculating a first position offset and a second position offset of the robot according to the zero moment point of the COM of the body; updating a position trajectory of the robot according to the first position offset and the second offset to obtain an updated position of the COM of the body; performing inverse kinematics analysis on the updated position of the COM of the body to obtain joint angles of the left leg and the right leg of the robot; and controlling the robot to move according to the joint angles.