An embodiment of the disclosure provides a warehouse robot control method and apparatus, a robot, and a warehouse system. The method includes: obtaining a container scheduling instruction of a first target container, where the container scheduling instruction includes a container type of the first target container; determining a container pose recognition algorithm according to the container type of the first target container, and recognizing pose information of the first target container based on the container pose recognition algorithm; and controlling the warehouse robot to pick up the first target container according to the pose information. For different types of containers, using corresponding pose recognition algorithms to determine pose information thereof and automatically picking up different types of the containers based on the pose information improve application range and efficiency of automatic pickup of the warehouse robot.

A method and system are herein disclosed wherein a robot handles objects that are large, unwieldy, highly-deformable, or otherwise difficult to contain and carry. The robot is operated to navigate an environment and detect and classify objects using a sensing system. The robot determines the type, size and location of objects and classifies the objects based on detected attributes. Grabber pad arms and grabber pads move other objects out of the way and move the target object onto the shovel to be carried. The robot maneuvers objects into and out of a containment area comprising the shovel and grabber pad arms following a process optimized for the type of object to be transported. Large, unwieldy, highly deformable, or otherwise difficult to maneuver objects may be managed by the method disclosed herein.

A system for assembling a vehicle platform includes a robotic assembly system having at least two robotic arms, a vision system capturing images of an assembly frame, and a control system configured to control the robotic assembly system to assemble the vehicle platform based on images from the vision system, force feedback from the at least two robotic arms, and a component location model. The control system is further configured to identify assembly features of a first component and a second component of the vehicle platform from the images, operate the robotic arms to orient the first component and the second component to respective nominal positions based on the images and the component location model, and operate the robotic arms to assemble the first component to the second component based on the force feedback.

A robotic drill system and a method of drilling with a robotic drill system. This includes inserting a tool head of the robotic drill within a hole of a drill template along an initial insertion trajectory with a robotic manipulator arm that is moved by at least one robotic actuator for causing robotic insertion of the tool head. In response to sensing binding of the tool head to a wall of the hole while inserting the tool head along the initial insertion trajectory, the disclosure includes stopping robotic insertion of the tool head and activating a self-centering device of the tool head to reorient the tool head to a corrected alignment of the tool head axis relative to the hole. The self-centering device may include an expandable collet.

A method of processing O-rings in an automated mass production system includes: (a) advancing an O-ring retainer toward a loading position in alignment with an output end of a feed device; (b) discharging a leading O-ring from the output end in electronic synchronization with advancement of the O-ring retainer to the loading position to initiate loading of the O-ring into the retainer prior to the retainer arriving at the loading position; (c) after loading the O-ring into the retainer, advancing the retainer away from the loading position toward an unloading position; and (d) moving an end effector in electronic synchronization with advancement of the retainer to the unloading position to synchronize arrival of the retainer at the unloading position with arrival of the end effector at a pick-up position in alignment with the O-ring at the unloading position for pick up of the O-ring by the end effector.

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 device that dispenses spacer material is disclosed. The device may be implemented in connection with a robotic palletization/depalletization system. The device may include a mounting hardware configured to mount the device on or adjacent to an end effector of a robotic arm, a communication interface configured to receive a control signal, and an actuator configured to dispense a quantity of spacer material from a supply of spacer material in response to the control signal.