A program creation apparatus acquires a work sequence executed by a robot, and creates a motion program based on the work sequence and a work information file in which information on work contained in the work sequence is recorded. Further, the work sequence is corrected from the work information file, and a new motion program based on a corrected new work sequence is created.

A method of the present disclosure includes (a) setting a limit value specifying a constraint condition with respect to a specific force control characteristic value detected in force control and an objective function with respect to a specific evaluation item relating to the work, (b) searching for an optimal value of the force control parameter using the objective function, and (c) determining a setting value of the force control parameter according to a result of the searching. The objective function has a form in which a penalty increasing according to an exceedance of the force control characteristic value from an allowable value smaller than the limit value is added to an actual measurement value of the evaluation item.

Utilizing an initial set of offline positive-only robotic demonstration data for pre-training an actor network and a critic network for robotic control, followed by further training of the networks based on online robotic episodes that utilize the network(s). Implementations enable the actor network to be effectively pre-trained, while mitigating occurrences of and/or the extent of forgetting when further trained based on episode data. Implementations additionally or alternatively enable the actor network to be trained to a given degree of effectiveness in fewer training steps. In various implementations, one or more adaptation techniques are utilized in performing the robotic episodes and/or in performing the robotic training. The adaptation techniques can each, individually, result in one or more corresponding advantages and, when used in any combination, the corresponding advantages can accumulate. The adaptation techniques include Positive Sample Filtering, Adaptive Exploration, Using Max Q Values, and Using the Actor in CEM.

A robot teaching system includes a teaching unit and a robot including a robotic arm and a robot controller. In the robot teaching system, a workpiece includes an internal space having an opening, and a target object of a work by the end effector exists in the internal space. The robot controller determines a possibility that the arm part interferes with an edge of the opening while the robotic arm is jogging or inching.

A vibration display device including a vibration acquisition unit that acquires a vibration state of a distal end section of a robot that is a robot in a simulation or in a real world, the distal end section being moved based on an operation program, and a vibration trajectory drawing unit that draws, on a display device, the vibration state along a trajectory of the distal end section of the robot or that draws, on the display device, the vibration state as the trajectory.

Systems and methods for classifying at least a portion of an image as being textured or textureless are presented. The system receives an image generated by an image capture device, wherein the image represents one or more objects in a field of view of the image capture device. The system generates one or more bitmaps based on at least one image portion of the image. The one or more bitmaps describe whether one or more features for feature detection are present in the at least one image portion, or describe whether one or more visual features for feature detection are present in the at least one image portion, or describe whether there is variation in intensity across the at least one image portion. The system determines whether to classify the at least one image portion as textured or textureless based on the one or more bitmaps.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.

Disclosed herein are an apparatus for automated communication between a robot and an artificial intelligence service. The method for automated communication between a robot and an artificial intelligence service is performed by an apparatus for automated communication between the robot and the artificial intelligence service, and includes generating bridge code and a container definition file based on a mapping rule defined between the robot and the artificial intelligence service, running a bridge container having an independent format based on the container definition file, executing the bridge code through the bridge container, and providing an automated communication environment by exchanging a message between the robot and the artificial intelligence service based on the bridge code.

Method for determining a grasping hand model suitable for grasping an object by receiving an image including at least one object; obtaining an object model estimating a pose and shape of the object from the image of the object; selecting a grasp class from a set of grasp classes by means of a neural network, with a cross entropy loss, thus, obtaining a set of parameters defining a coarse grasping hand model; refining the coarse grasping hand model, by minimizing loss functions referring to the parameters of the hand model for obtaining an operable grasping hand model while minimizing the distance between the finger of the hand model and the surface of the object and preventing interpenetration; and obtaining a mesh of the hand represented by the enhanced set of parameters.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for state estimation in a robotics system. One of the systems includes an execution subsystem configured to drive one or more robots in an operating environment including continually evaluating a plurality of execution predicates, wherein each execution predicate comprises a rule having a predicate value, and wherein, whenever a state value that satisfies the predicate value of the predicate is detected by the execution subsystem, the execution subsystem is configured to trigger a corresponding action to be performed in the operating environment by the one or more robots. A state estimator is configured to continually execute a state estimation function using one or more sensor values or status messages obtained from the operating environment and to automatically update a discrete state value for a first execution predicate of the plurality of execution predicates evaluated by the execution subsystem.