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.

The present disclosure discloses a robot control method as well as an apparatus, and a robot using the same. The method includes: obtaining a human pose image; obtaining pixel information of key points in the human pose image; obtaining three-dimensional positional information of key points of a human arm according to the pixel information of the preset key points; obtaining a robotic arm kinematics model of a robot; obtaining an angle of each joint in the robotic arm kinematics model according to the three-dimensional positional information of the key points of the human arm and the robotic arm kinematics model; and controlling an arm of the robot to perform a corresponding action according to the angle of each joint. The control method does not require a three-dimensional stereo camera to collect three-dimensional coordinates of a human body, which reduces the cost to a certain extent.

The present disclosure provides a humanoid robot and its control method and computer readable storage medium. The method includes: obtaining a current torque of a sole of the humanoid robot, an inclination angle of the sole, an inclination angle of a first joint of the humanoid robot, and an inclination angle of a second joint of the humanoid robot; calculating current feedforward angular velocities of motors of the first and second joints through the obtained information; calculating feedback angular velocities of the motors of the first and second joints; and obtaining inclination angles of the joints based on the feedforward angular velocities of the motors and the feedback angular velocities of the motors, and performing, through the motor of the second joint, a deviation control on the joints according to the inclination angles of the joints.

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.

The present disclosure provides a robot control method, apparatus and a storage medium with the same. The method includes: obtaining a serial number and a rotational angle parameter of a first servo corresponding to a preset motion frame portion of a first motion; obtaining a serial number of a second servo located symmetrical to the first servo; receiving an instruction for mirroring the preset motion frame portion; performing a preset mirroring processing on a rotational angle parameter of the second servo according to the instruction; and storing the mirrored rotational angle parameter in a motion frame portion of a second motion; performing the first motion and the second motion. In the above-mentioned manner, the difficulty in adjusting the motion frame in the mirroring operation of the robot is largely simplified, and the accuracy and efficiency of the mirroring operation are improved.

A method for controlling a robotic device based on observed object locations is presented. The method includes observing objects in an environment. The method also includes generating a probability distribution for locations of the observed objects. The method further includes controlling the robotic device to perform an action when an object is at a location in the environment with a location probability that is less than a threshold.

[Problem] To optimally switch tasks of a robot depending on the situation. [Solution] According to the present disclosure, there is provided a robot control apparatus including: a determination unit configured to determine whether or not an end condition of a task is satisfied when a robot performs the task; and a switching unit configured to perform switching to a next task corresponding to the end condition in a case where the end condition is satisfied. With this configuration, it is possible to optimally switch tasks of the robot depending on the situation.

A large area surveillance method is based on weighted double deep Q-learning. A robot which of Q-value table including a Q.sub.A-value table and Q.sub.B-value table is provided, an unidentified object enters a large space to trigger the robot, and the robot perceives a current state s and determines whether the current state s is a target state, if yes, the robot reaches a next state and monitors the unidentified object, and if not, the robot reaches a next state, obtains a reward value according to the next state, selectively updates a Q.sub.A-value or Q.sub.B-value with equal probability, and then updates a Q-value until convergence to obtain an optimal surveillance strategy. The problems of a limited surveillance area and camera capacity are resolved, and the synchronization of multiple cameras doesn't need to be considered, and thus the cost is reduced. A large area surveillance robot is also disclosed.

Provided is a link-sequence mapping device capable of automatically mapping a model link-sequence to a link sequence of an arbitrarily defined robot. The link-sequence mapping device (1) is equipped with: a reception unit (11) for receiving model link-sequence information indicating the positions of respective links included in a model link-sequence; an identification unit (14) for identifying, by using the model link-sequence information, coordinate values of predetermined multiple positions in the model link-sequence; a calculation unit (15) for calculating robot link-sequence information, that is, information about the positions of respective links included in a robot link-sequence, such that objective functions corresponding to the respective distances between the identified multiple positions and corresponding multiple positions in the robot link-sequence are reduced; and an output unit (17) for outputting information about angles of respective joints in the robot link-sequence which are determined in accordance with the calculated robot link-sequence information.

A display apparatus includes an acquisition unit acquiring a basic motion program, a life calculation unit calculating a first life of a robot arm when a motion is performed based on the basic motion program acquired by the acquisition unit, a creation unit creating a corrected motion program for setting a life of the robot arm to be a second life longer than the first life using the same configuration of the robot arm and work start position and work end position of a control point as those of the basic motion program and changing a position of a proximal end of the robot arm, and a display unit displaying a position of the proximal end of the robot arm in the corrected motion program.