A robot joint configuration determining method, a robot using the same, and a computer readable storage medium are provided. The method includes: simulating a joint model of a first joint of the robot using first motion deviation data to obtain first result data; simulating the joint model using second motion deviation data to obtain second result data; taking the motion deviation data corresponding to one of the first result data and the second result data meeting one or more preset conditions as a target motion deviation data for the first joint; and determining type information of a reducer in a configuration information of the first joint based on the target motion deviation data. In the present disclosure, the motion deviation of the first joint that is relatively accurate can be obtained through the results of the two simulations.

A control unit to ascertain one or more parameters of a control program and/or of a control system for a robot manipulator, wherein the control unit includes: an interactive operating unit to display a first adjustment element and a specified region for the first adjustment element, wherein the first adjustment element is moveable within the specified region via an input of a user, the interactive operating unit further to detect a user-specified position of the first adjustment element and transmit the user-specified position; and a computing unit to receive the user-specified position and ascertain weightings for a specified cost function as a function of the position, wherein a sum of the weightings is constant for all positions of the adjustment element, the computing unit further to ascertain the parameters of the control program and/or of the control system for the robot manipulator based on the cost function.

A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

For a kinematic modelled in a kinematics coordinate system by hingedly interconnected single axles, a method calculates a braking region possibly covered by at least one of the single axles connected to an origin of the kinematics coordinate system and at least one of the single axles moving relative to the origin. In the event of a braking process, for a point that is coupled to a single axle, at least one virtual end position of the point is determined from an initial position of the point, a vectorial speed of at least one single axle, and a minimum deceleration of at least one single axle. The braking region of the point is determined using an envelope of the initial position and the at least one virtual end position, the extent of the envelope being calculated from the initial position and the at least one virtual end position.

Example implementations described herein are directed to a simulation environment for a real world system involving one or more robots and one or more sensors. Scenarios are loaded into a simulation environment having one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more virtual system to train a control strategy model from reinforcement learning, which is subsequently deployed to the real world environment. In cases of failure of the real world environment, the failures are provided to the simulation environment to generate an updated control strategy model for the real world environment.

Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).

A computing device stores a kinematic model of a robot comprising an articulated arm and a tool coupled to the arm. The kinematic model comprises a plurality of active joints corresponding to a plurality of actuated joints of the articulated arm, and one or more passive joint. For each passive joint, a nominal joint position and a corresponding tolerance margin is defined, for simulating a tolerance margin applicable to a nominal position and orientation of the tool with respect to an object processed by the tool. The computing device determines a 3D model of the object, determines a toolpath of the tool for performing a task on the object and calculates a trajectory of the articulated arm based on the toolpath, the kinematic model and the 3D model of the object. The calculation takes into account the nominal joint position and the tolerance margin of each passive joint.

A digital twin modeling method to assemble a robotic teleoperation environment, including: capturing images of the teleoperation environment; identifying a part being assembled; querying the assembly assembling order to obtain a list of assembled parts according to the part being assembled; generating a three-dimensional model of the current assembly from the list and calculating position pose information of the current assembly in an image acquisition device coordinate system; loading a three-dimensional model of the robot, determining a coordinate transformation relationship between a robot coordinate system and an image acquisition device coordinate system; determining position pose information of the robot in an image acquisition device coordinate system from the coordinate transformation relationship; determining a relative positional relationship between the current assembly and the robot from position pose information of the current assembly and the robot in an image acquisition device coordinate system; establishing a digital twin model of the teleoperation environment.

A robot control method, and associated robot controllers and robots operating with such methods and controllers, providing real-time vibration suppression. The control method involves learning to support real-time, vibration-suppressing control. The method uses state-of-the-art machine learning techniques in conjunction with a differentiable dynamics simulator to yield fast and accurate vibration suppression. Vibration suppression using offline simulation approaches that can be computationally expensive may be used to create training data for the controller, which may be provide by a variety of neural network configurations. In other cases, sensory feedback from sensors onboard the robot being controlled can be used to provide training data to account for wear of the robot's components.

A nervous system emulator engine includes working computational models of the vertebrate nervous system to generate lifelike animal behavior in a robot. These models include functions representing several anatomical features of the vertebrate nervous system, such as spinal cord, brainstem, basal ganglia, thalamus and cortex. The emulator engine includes a hierarchy of controllers in which controllers at higher levels accomplish goals by continuously specifying desired goals for lower-level controllers. The lowest levels of the hierarchy reflect spinal cord circuits that control muscle tension and length. Moving up the hierarchy into the brainstem and midbrain/cortex, progressively more abstract perceptual variables are controlled. The nervous system emulator engine may be used to build a robot that generates the majority of animal behavior, including human behavior. The nervous system emulator engine may also be used to build working models of nervous system functions for clinical experimentation.