Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an industrial plant controller that controls operation of an industrial plant. In one aspect, a method comprises generating training data using an industrial plant simulation model that simulates operation of the industrial plant. The industrial plant controller is trained by a reinforcement learning technique using the training data. The industrial plant controller is configured to process an input comprising a state vector characterizing a state of the industrial plant in accordance with a plurality of industrial plant controller parameters to generate an action selection policy output that defines a control action to be performed to control the operation of the industrial plant.

A robot control apparatus causes one or more robots to perform a predetermined task formed by a plurality of task processes. The robot control apparatus includes first control units each configured to control an operation of the one or more robots for each task process of the plurality of task processes, and a second control unit configured to specify a combination and an order to execute the first control units in the plurality of task processes and cause each of the first control units to operate in accordance with the combination and the order.

A system and method of instructing a device is disclosed. The system includes a signal source for providing at least one visual signal where the at least one visual signal is substantially indicative of at least one activity to be performed by the device. A visual signal capturing element captures the at least one visual signal and communicates the at least one visual signal to the device where the device interprets the at least one visual signal and performs the activity autonomously and without requiring any additional signals or other information from the signal source.

A system and method for personalization of an interaction between a social robot and a user. The method comprises collecting, by one or more of a plurality of sensors of the social robot, a first set of sensory data from the user; determining, based on the collected first set of sensory data, a first state of the user; determining whether the first state of the user requires a change to a second state of the user; performing, by the social robot, a first operational schema selected from a plurality of operational schemas based on an influence score of the first operational schema upon determination that the first state of the user requires a change to the second state of the user, wherein the influence score of the first operational schema is determined based on the likelihood of the first operational schema to cause a user to change from the first state to a second state; collecting, by one or more of the plurality of sensors of the social robot, a second set of sensory data from the user; and determining, based on the collected second set of sensory data, an actual state of the user.

A system and method for a method for optimizing machine learning algorithms for monitoring industrial machine operation, including: monitoring at least one industrial machine behavioral model of at least one industrial machine; identifying at least a first ambiguous segment of the at least one industrial machine behavioral model having a first set of characteristics, and identifying a corrective solution recommendation associated with the first ambiguous segment; identifying at least a second ambiguous segment of the at least one industrial machine behavioral model having a second set of characteristics; determining if a similarity between the first set of characteristics and the second set of characteristics exceed a predetermined threshold; and updating a machine learning algorithm of the at least one industrial machine behavioral model to associate the corrective solution recommendation to the second ambiguous segment when it is determined that the similarity has exceed the predetermined threshold.

A computer-implemented method for generating and deploying a reinforced learning model to train a chatbot. The method includes selecting a plurality of conversations, wherein each conversation includes an agent and a user. The method includes identifying, in each of the conversations, a set of turns and on or more topics. The method further includes associating one or more topics to each turn of the set of turns. The method includes, generating a conversation flow for each conversation, wherein the conversation flow identifies a sequence of the topics. The method includes applying an outcome score to each conversation. The method includes creating a reinforced learning (RL) model, wherein the RL model includes a Markov is based on the conversation flow of each conversation and the outcome score of each conversation. The method includes deploying the RL model, wherein the deploying includes sending the RL model to a chatbot.

Mitigating the reality gap through utilization of technique(s) that enable compliant robotic control and/or compliant robotic contact to be simulated effectively by a robotic simulator. The technique(s) can include, for example: (1) utilizing a compliant end effector model in simulated episodes of the robotic simulator; (2) using, during the simulated episodes, a soft constraint for a contact constraint of a simulated contact model of the robotic simulator; and/or (3) using proportional derivative (PD) control in generating joint control forces, for simulated joints of the simulated robot, during the simulated episodes. Implementations additionally or alternatively relate to determining parameter(s), for use in one or more of the techniques that enable effective simulation of compliant robotic control and/or compliant robotic contact.

This invention provides a robot control device for controlling a robot configured to perform a predetermined operation, where the robot control device comprises an acquisition unit configured to acquire a plurality of images captured by a plurality of image capturing devices including a first image capturing device and a second image capturing device different from the first image capturing device; and a specification unit configured to use the plurality of captured images acquired by the acquisition unit as inputs to a neural network, and configured to specify a control instruction for the robot based on an output as a result from the neural network.

Provided is a machining equipment system including machining equipment that performs machining of a workpiece; a control device that controls the machining equipment on the basis of a machining condition; a state obtaining device that obtains a state of the machining equipment during the machining; an inspection device that inspects the workpiece after the machining; and a machine learning device that performs machine learning on the basis of a result of inspection by the inspection device and the state of the machining equipment, obtained by the state obtaining device, wherein the machine learning device modifies the machining condition on the basis of a result of the machine learning so as to improve the machining accuracy of the workpiece or so as to minimize the defect rate of the workpiece.

Provided are systems and methods for training a robot. The method commences with collecting, by the robot, sensor data from a plurality of sensors of the robot. The sensor data may be related to a task being performed by the robot based on an artificial intelligence (AI) model. The method may further include determining, based on the sensor data and the AI model, that a probability of completing the task is below a threshold. The method may continue with sending a request for operator assistance to a remote computing device and receiving, in response to sending the request, teleoperation data from the remote computing device. The method may further include causing the robot to execute the task based on the teleoperation data. The method may continue with generating training data based on the sensor data and results of execution of the task for updating the AI model.