The present invention features a computer-implemented method of planning a processing path relative to a three-dimensional workpiece for a plasma arc cutting system coupled to a robotic arm. The method includes receiving input data from a user comprising (i) Computer-Aided Design (CAD) data for specifying a desired part to be processed from the three-dimensional workpiece, and (ii) one or more desired parameters for operating the plasma arc cutting system. A plurality of features of the desired part to be formed on the three-dimensional workpiece are identified based on the CAD data. The method also includes dynamically filtering a library of cut charts based on the plurality of features and the desired operating parameters to determine a recommended cut chart for processing the plurality of features. The method further includes generating the processing path based on the recommended cut chart and the plurality of features to be formed.

Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Systems and a method are provided for programming a cobot for a plurality of cells of an industrial environment. A physical cobot is provided within a lab cell comprising physical lab objects. A virtual simulation system receives information inputs on a virtual cobot representing the physical cobot, regarding a virtual lab cell comprising virtual lab objects, and on a plurality of virtual industrial cells comprising virtual industrial objects. Inputs are received from the physical cobot's movement during teaching whereby the physical cobot is moved in the lab cell to the desired position(s) while providing, via a user interface, a visualization of the virtual cobot's movement within a meta cell generated by superimposing the plurality of virtual industrial cells with the virtual lab cell, so that collisions with any object are minimized. A robotic program is generated based on the received inputs of the physical cobot's movement.

A method for controlling movement trajectories of a robot, the method including predicting, in an offline mode, values of at least one parameter related to the execution of alternative movement trajectories between a first position of the robot and a second position of the robot; selecting, in the offline mode, a movement trajectory based on the predicted values of the at least one parameter; and executing the selected movement trajectory by the robot. A control system for controlling movement trajectories of a robot is also provided.

Solutions for multi-robot configurations are co-optimized, to at least some degree, across a set of non-homogenous parameters based on a given set of tasks to be performed by robots in a multi-robot operational environment. Non-homogenous parameters may include two or more of: the respective base position and orientation of the robots, an allocation of tasks to respective robots, respective target sequences and/or trajectories for the robots. Such may be executed pre-runtime. Output may include for each robot: workcell layout, an ordered list or vector of targets, optionally dwell time durations at respective targets, and paths or trajectories between each pair of consecutive targets. Output may provide a complete, executable, solution to the problem, which in the absence of variability in timing, can be used to control the robots without any modification. A genetic algorithm, e.g., Differential Evolution, may optionally be used in generating a population of candidate solutions.

An information processing device converts first information for manipulating a robot model inputted into a manipulation terminal connected with a simulation device configured to execute a simulation for causing the robot model to perform a simulated operation and operated by a remote user of the simulation device, into second information for manipulating the robot model of the simulation device, operates the simulation device according to the second information, and causes the manipulation terminal to present information on the operation of the robot model of the simulation device configured to operate according to the second information.

A robot system including a robot apparatus and an imaging apparatus includes a control apparatus configured to control the robot apparatus and the imaging apparatus, and the control apparatus controls, based on a path in which a predetermined part of the robot apparatus is moved, a movement of the imaging apparatus to image the predetermined part even if the robot apparatus is moved.

The present disclosure provides describes to train a multi policy ML model to control robots in a multi-robot system in collaborating to perform a task. For example, trajectories associated with manipulating an object to perform the collaborative task can be determined and an ML model trained to output control actions for the robots in the multi-robot system to collaborate to complete the task.

A method, system and non-transitory instructions for control input, comprising, taking an integral of an output value from a Motion Decision Neural Network for a movable joint to generate an integrated output value. Generating a subsequent output value using a machine learning algorithm that includes a sensor value and the integrated output value as inputs to the Motion Decision Neural Network and imparting movement with the moveable joint according to an integral of the subsequent output value.

Systems and a method determine a sequence of joint values of an external axis along a sequence of targets. Inputs are received, including robot representation, tool representation, sequence of targets, kinematics of the axis joints, and/or type of robot-axis motion. For each target, it is generated at least one weight factor table representing, for each available configuration of the axis joint motion, a combined effort of the robot motion and the axis motion depending on the type of combined robot-axis motion. Valid weight factor values of the table are determined by simulating collision free trajectories for reaching the target. The sequence of joint values of the at least one external axis is determined by finding from the weight factor table a sequence of joint values for which the sum of their corresponding weight factors for reaching the target location sequence is minimized.