A computer-implemented dynamic supporting base creation method that interacts with a three-dimensional (3D) printer that prints an object, the method including providing a physical support, via a first robotic gripper, for an object during three-dimensional (3D) printing using a printing head of the 3D printer and transferring the object to a second robotic gripper to provide a physical support at a different location on the object.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing a plan for one or more robots using a process definition graph. One of the methods includes receiving a process definition graph for a robot, the process definition graph having a plurality of action nodes. One or more of the action nodes are motion nodes that represent a motion to be taken by the robot from a respective start location to an end location. It is determined that a motion node satisfies one or more splitting criteria, and in response to determining that the motion node satisfies the one or more splitting criteria, the process definition graph is modified. Modifying the process definition graph includes splitting the motion node into two or more separate motion nodes whose respective paths can be scheduled independently.

A method and corresponding apparatus for collision-free motion planning of a first manipulator in a first working space and a second manipulator in a second working space, wherein the first and second working spaces at least partially overlap. The method includes the steps of importing a first dynamic roadmap for a first configuration space of the first manipulator, wherein the first dynamic roadmap includes a first search graph and a first mapping between the first working space and the first search graph, and importing a second dynamic roadmap for a second configuration space of the second manipulator, wherein the second dynamic roadmap includes a second search graph and a second mapping between the second working space and the second search graph. Furthermore, the motion of the first manipulator and the second manipulator are coordinated based on the first dynamic roadmap and the second dynamic roadmap.

Disclosed is a deep reinforcement learning apparatus and method for a pick-and-place system. According to the present disclosure, a simulation learning framework is configured to apply reinforcement learning to make pick-and-place decisions using a robot operating system (ROS) in a real-time environment, thereby generating stable path motion that meets various hardware and real-time constraints.

System for automatically manipulating primary packaging in secondary packaging, comprising a robot having at least one robot arm with a clamping gripper installed at a tool centre point, wherein each tool centre point has a force-torque sensor, an image recording module for recording images of at least the upper segment of the primary packaging, comprising at least two stereo cameras for recording 3-D images, and one or more processors for providing a three-dimensional point cloud, controlling the image recording module and controlling the robot on the basis of the analysis of the three-dimensional point cloud and the measurements from the force-torque sensors.

In an embodiment, a method includes determining a given material to manipulate to achieve a goal state. The goal state can be one or more deformable or granular materials in a particular arrangement. The method further includes, for the given material, determining, a respective outcome for each of a plurality of candidate actions to manipulate the given material. The determining can be performed with a physics-based model, in one embodiment. The method further can include determining a given action of the candidate actions, where the outcome of the given action reaching the goal state is within at least one tolerance. The method further includes, based on a selected action of the given actions, generating a first motion plan for the selected action.

A first robot for handling at least one first workpiece in a first processing operation of the apparatus, a second robot cooperating with the first robot for processing the at least one first workpiece in the first processing operation, and at least one first workpiece holder for holding the at least one first workpiece during the first processing operation. In order to improve robot-assisted processing of workpieces which differ from one another, the first robot handles at least one second workpiece and the second robot processes the at least one second workpiece in a second processing operation of the apparatus. The first robot or the second robot in order, in a changeover operation of the apparatus, to replace the at least one first workpiece holder automatically by at least one second workpiece holder for holding the at least one second workpiece during the second processing operation.

A method of controlling at least one of a first robot and a second robot to collaborate within a system, the first robot and the second robot being physically separate to one another, the method including: receiving sensed data associated with the second robot; determining position and/or orientation of the second robot using the received sensed data; determining an action for the second robot using the determined position and/or orientation of the second robot; and providing a control signal to the second robot to cause the second robot to perform the determined action to collaborate with the first robot.

A multiaxial robot of multitasking includes a base, a plurality of arms, at least one wrist, a first engaging structure, and a second engaging structure. The arms are sequentially connected from the base, and any adjacent two of the base and the arms are configured to rotate relative to each other. The wrist is connected to the farthest arm arranged relative to the base and configured to rotate relative to the connected arm. The first engaging structure is disposed on the wrist and configured to connect a first tool. The second engaging structure is disposed on one of the arms and configured to connect a second tool.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.