Human-in-the-loop robot training using artificial intelligence (AI) for robotic process automation (RPA) is disclosed. This may be accomplished by a listener robot watching interactions of a user or another robot with a computing system. Based on the interactions by the user or robot with the computing system, the robot may be improved and/or personalized for the user or a group of users.

A robot control system includes robot controller circuitry that controls a robot, and host controller circuitry that communicates with the robot controller circuitry. The host controller circuitry further executes a control program, and transmits a command according to an execution result of the control program to the robot controller circuitry, and the robot controller circuitry further receives the command from the host controller circuitry, and executes pre-processing according to the command.

A communication system according to an embodiment of the present invention includes a robot control device, a programmable logic controller for establishing communication with the robot control device, and a communication setting device that is loaded with a configuration file to define communication parameters used in the communication. The communication setting device sets the communication parameters to the programmable logic controller. The robot control device includes a file output unit for outputting the configuration file depending on an internal state of the robot control device.

Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).

A robot system includes at least one robot configured to interact with a plurality of operators in a warehouse. The robot has a proximity detector configured to detect the presence of an operator of the plurality of operators when they are within a predetermined distance of the at least one robot. There is a processor configured to retrieve from a memory a set of operator interaction preferences for the operator detected. And, there is a display device configured to allow the at least one robot to interact with the detected operator based on the set of operator interaction preferences of the detected operator.

A system for identifying and tracking performance of operators in a warehouse. The system comprises at least one robot configured to interact with the operators in the warehouse. The at least one robot includes a first transceiver, a proximity detector, and a memory. The first transceiver defines a zone surrounding the robot and the proximity detector is coupled to the first transceiver. The proximity detector is configured to detect entry, into the zone, of an operator and to detect exit of the operator from the zone. The memory contains information identifying said operators who have entered and exited the zone.

Human-in-the-loop robot training using artificial intelligence (AI) for robotic process automation (RPA) is disclosed. This may be accomplished by a listener robot watching interactions of a user or another robot with a computing system. Based on the interactions by the user or robot with the computing system, the robot may be improved and/or personalized for the user or a group of users.

Human-in-the-loop automation training using artificial intelligence (AI) for agentic automation is disclosed. This may be accomplished by a listener watching interactions of a user or an AI agent with a computing system. Based on the interactions by the user or the AI agent with the computing system, the automation may be improved and/or personalized for the user or a group of users.