Computerized engineering tool and methodology to develop neural skills for computerized autonomous systems, such as a robotics system (50), are provided. A disclosed computerized engineering tool (10) may involve an integrated arrangement of respective modular functionalities arranged in a closed loop, such as may include a physics engine (14), a neural data editor (16), an experiment editor (18), a neural skills editor (20), and a machine learning environment (22). Disclosed embodiments are conducive to cost-effectively simplifying development efforts involving neural skills, such as by reducing the time involved to develop the neural skills involved in any given robotics system and by reducing the level of expertise involved to develop neural skills.

An information processing apparatus (100) includes an estimation unit (133) configured to estimate an external force estimation value which is an estimation value for an external force that acts on an object to be driven by performing a process based on a machine learning model with a force estimation value and an external force response value as an input, the force estimation value being an estimation value for a force that acts on the object to be driven, the external force response value being based on a value of an external force detected by a force sensor configured to detect the external force that acts on the object to be driven.

A controller controls a motion of an object performing a task for changing a state of the object from a start state to an end state while avoiding collision of the object with an obstacle according to an optimal trajectory determined by solving an optimization problem of the dynamics of the object producing an optimal trajectory for performing the task subject to constraints on a solution of first-order stationary conditions modeling a minimum distance between the convex hull of the object and the convex hull of the obstacle using complementarity constraints.

An apparatus for constructing kinematic information of a robot manipulator is provided. The apparatus includes: a robot image acquisition part for acquiring a robot image containing shape information and coordinate information of the robot manipulator; a feature detection part for detecting the type of each of a plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint using a feature detection model generated through deep learning based on the robot image containing shape information and coordinate information; and a variable derivation part for deriving Denavit-Hartenberg (DH) parameters based on the type of each of the plurality of joints of the robot manipulator and the three-dimensional coordinates of the joint.

An automated charging system for an electric vehicle is disclosed that includes a plug with a built-in camera assembly. The camera assembly captures images of a charging port of the electric vehicle, which are processed by one or more processors to estimate the location of the charging port relative to the plug. A control system generates signals for one or more actuators to move the plug relative to the charging port, thereby inserting the plug into the charging port to connect a power supply to the electric vehicle. The images may be processed via an image recognition algorithm and/or one or more machine learning algorithms. In an embodiment, the images are processed by a neural network to estimate the location of the charging port relative to the plug. The plug can also include a tapered structure to make fine adjustments to the position or orientation of the plug during connection.

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.

A robot includes a robot body, a hand, an arm, and a controller. The hand includes a fixed frame that is fixed to the arm, a first camera that is attached to the fixed frame, a movable frame that is rotatable with respect to the fixed frame, gripping portions that are attached to the movable frame to grip an article having a front surface facing the robot and a back surface opposite to the front surface, and a driver that rotates the movable frame. The gripping portions grip the article in a state where the back surface is opened, and shift from a first state where the article is gripped to a second state where the back surface of the article is able to be captured by the first camera by the rotation of the movable frame.

A machine learning device, which learns an operation program of a robot, includes a state observation unit which observes as a state variable at least one of a shaking of an arm of the robot and a length of an operation trajectory of the arm of the robot; a determination data obtaining unit which obtains as determination data a cycle time in which the robot performs processing; and a learning unit which learns the operation program of the robot based on an output of the state observation unit and an output of the determination data obtaining unit.

The present invention is a control device which includes a filter unit for performing an attenuation process at a predetermined frequency on a control input based on a predetermined target command, generates the control input through model predictive control executed by a model predictive control unit and causes an output of a predetermined control object to follow the predetermined target command. A prediction model defines a correlation between the control input and predetermined extended state variables including a state variable related to a predetermined control object and a predetermined filter state variable related to the filter unit, and a predetermined evaluation function for model predictive controls configured to calculate a state quantity cost that is a stage cost with respect to state variables except the predetermined filter state variable among the predetermined extended state variables, and a control input cost that is a stage cost related to the control input.

An artificial intelligence server for determining a route of a robot includes a communication unit and a processor. The communication unit is configured to receive image data for a control area from the robot or a camera installed inside the control area. The processor is configured to calculate a current density for the control area from the image data, calculate a future density for the control area using the calculated current density, determine a priority for each of group areas included in the control area based on the calculated future density, and determine the route of the robot based on the determined priority.