A dual-manipulator control method is configured to be used in a dual-manipulator control system including a first manipulator, a second manipulator, and a central control module. The first manipulator and the second manipulator are controlled by the central control module, and the central control module is configured to execute the dual-manipulator control method. The dual-manipulator control method includes: generating a first instruction sequence to control the first manipulator and a second instruction sequence to control the second manipulator; and controlling the first manipulator and the second manipulator based on the first instruction sequence and the second instruction sequence. Thus, the working efficiency is improved.

A method for training a neural network to derive, from a force and a moment exerted on an object when pressed on a plane in which an insertion for inserting the object is located, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object or the part of the object held by the robot touches a plane in which the insertion is located, controlling the robot to move to the position, controlling the robot to press the object onto the plane, measuring the force and moment experienced by the object, scaling the pair of force and moment by a number randomly chosen between zero and a predetermined positive maximum number and labelling the scaled pair by a movement vector between the position and the insertion, and training the neural network using the labelled pairs of force and moment.

A method for controlling a robot to insert an object into an insertion. The method includes controlling the robot to hold the object, generating an estimate of a target position to insert the object into the insertion, controlling the robot to move to the estimated target position, taking a camera image using a camera mounted on the robot after having controlled the robot to move to the estimated target position, feeding the camera image into a neural network which is trained to derive, from camera images, movement vectors which specify movements from the positions at which the camera images are taken to insert objects into insertions and controlling the robot to move according to the movement vector derived by the neural network from the camera image.

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector for the robot to insert an object into an insertion. The method includes controlling the robot to hold the object, bringing the robot into a target position in which the object is inserted in the insertion, for a plurality of positions different from the target position controlling the robot to move away from the target position to the position, taking a camera image by the camera and labelling the camera image by a movement vector to move back from the position to the target position and training the neural network using the labelled camera images.

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector to insert an object into an insertion. The method includes, for a plurality of positions in which the object held by the robot touches a plane in which the insertion is located controlling the robot to move to the position, taking a camera image by the camera and labelling the camera image with a movement vector between the position and the insertion in the plane and training the neural network using the labelled camera images.

A robot system includes a robot, an image capture apparatus, an image processing portion, and a control portion. The image processing portion is configured to specify in an image of a plurality of objects captured by the image capture apparatus, at least one area in which a predetermined object having a predetermined posture exists, and obtain information on position and/or posture of the predetermined object in the area. The control portion is configured to control the robot, based on the information on position and/or posture of the predetermined object, for the robot to hold the predetermined object.

A training data generation device includes a virtual scene generation unit, an orthographic virtual camera, an object-occlusion determination unit, an object-occlusion determination unit and a perspective virtual camera. The virtual scene generation unit is configured for generating a virtual scene, wherein the virtual scene comprises a plurality of objects. The orthographic virtual camera is configured for capturing a vertical projection image of the virtual scene. The object-occlusion determination unit is configured for labeling an occluded-state of each object according to the vertical projection image. The perspective virtual camera is configured for capturing a perspective projection image of the virtual scene. The training data generation unit is configured for generating a training data of the virtual scene according to the perspective projection image and the occluded-state of each object.

A method includes: (a) executing prior learning of the machine learning model, using simulation data of an object; (b) capturing a first image of the object from a first direction of image capture; (c) recognizing a first position and attitude of the object from the first image, using the machine learning model already learned through the prior learning; (d) performing a correctness determination about the first position and attitude; (e) capturing a second image of the object from a second direction of image capture that is different from the first direction of image capture when it is determined that the first position and attitude is correct, then converting the first position and attitude according to a change from the first direction of image capture to the second direction of image capture and thus calculating a second position and attitude, and assigning the second position and attitude to the second image and thus generating training data; and (f) changing an actual position and attitude of the object and repeating the (b) to (e).

Devices, systems, and methods for autonomously sorting dirty laundry articles into batched loads for washing are described. For example, an autonomous sorting device includes an enclosed channel including a plurality of sequential work volumes and a stationary floor extending between an inlet end and an outlet end of the channel, a plurality of arms disposed in series along the enclosed channel for rotating, tilting, extending, and retracting a terminal gripper of each arm into an associated work volume for grabbing at least one of a plurality of deformable dirty laundry articles and passing the at least one deformable laundry article to an adjacent work volume for grasping and hoisting by an adjacent arm. The device includes an inlet orifice for receiving the dirty laundry articles into the enclosed channel and an outlet orifice adjacent the outlet end through which each separated deformable article exits the enclosed channel into sorting bins.

A control system comprises one or more processors. The one or more processors are configured to extract a feature of a person in an image captured by a camera, classify the person into a preset first group or a preset second group based on the feature, estimate a moving speed of the person belonging to the second group, and switch, based on the moving speed, a mode between a high-load mode for performing a high-load process and a low-load mode for performing a process with a load lower than a load in the high-load mode.