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).

Embedded and/or pooled robotic process automation (RPA) robots are disclosed. A master robot initiates one or more RPA robots in a deterministic and/or probabilistic manner. For instance, when a step in an RPA workflow of the master robot is encountered where an action is not clear, some data is missing, there are multiple possible branches, etc., one or more embedded and/or pooled minion robots may be called upon by the master robot to determine the next action to take, to retrieve missing data, to determine which branch is appropriate, etc. The master robot may perform orchestration functionality with respect to the minion robot(s).

Some embodiments provide a user-friendly interface enabling a non-technical user to automatically provision a robotic process automation (RPA) environment on a target computing system such as a cloud computing platform or an on-premises server. The provisioned environment may include all necessary components (e.g., virtual machines, robots, robot orchestrator, databases, network links) to execute a robotic task. The provisioned environment is terminated automatically after or at a time specified by the user. The described systems and methods are particularly useful at trade shows or other events, in order to simplify and speed up the demonstration of RPA software capabilities to different customers planning RPA software deployment in different contexts, including on different cloud platforms or hybrid contexts combining cloud with on-premises host platforms.

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).

Computerized robotic process automation (RPA) methods and systems that increase the flexibility and lower the cost with which RPA systems may be deployed are disclosed herein. In one embodiment, an RPA system and method avoids the need for preinstalled RPA software on a device employed by a user to create and/or execute software robots to perform RPA. In another embodiment, an RPA system and method provides a capability to execute software robots that may have been encoded in one or more programming languages to execute on an operating system different than that employed by a server of the RPA system.

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.

An access management robot facilitation system facilitates a robot to execute access management tasks on a target system.

A control apparatus includes a memory and a processor. The processor is configured to control a distal end portion of a robot to rotate around first and second distal end axes, perform a first control mode in which the distal end portion rotates around the first and second distal end axes, perform a second control mode in which the distal end portion rotates around the first distal end axis and does not rotate around the second distal end axis, switch the first control mode to the second control mode when the first distal end axis is perpendicular to a working surface, and control the distal end portion to work while performing the second control mode and maintaining a state in which the first distal end axis is perpendicular to the working surface after the first control mode is switched to the second control mode.

Computerized RPA methods and systems that increase the flexibility and lower the cost with which RPA systems may be deployed are disclosed herein. In one embodiment, an RPA system and method avoids the need for preinstalled RPA software on a device employed by a user to create and/or execute software robots to perform RPA. In another embodiment, an RPA system and method provides a capability to execute software robots that may have been encoded in one or more programming languages to execute on an operating system different than that employed by a server of the RPA system.

A robot control method, a device, and a remote control system. A mobile terminal establishes a connection to a robot by scanning a two-dimensional code and by a local area network. A remote monitoring and control center connects to the mobile terminal by a wide area network, and when the mobile terminal and the remote monitoring and control center send information to each other, the mobile terminal breaks off connection with the robot, thereby ensuring information transmission security. Operation instruction information from a user on an operation interface is acquired, the operation instruction information comprising a control parameter and an action execution parameter, and on based on the set control parameter and the set action execution parameter, first control instruction information is generated, and sent to the robot by a wireless communications network, in order to cause the robot to execute a corresponding action based on the first control instruction information.