A robot cluster scheduling system includes a user layer, an intermediate layer, an application layer, a plug-in layer and a data persistence layer. The intermediate layer includes a processor mapping module and a state acquisition module. The application layer includes a task scheduling module and a traffic scheduling module. The plug-in layer includes a task solving engine and a traffic planning engine. The task solving engine is configured to determine a target robot according to a parameter of a task and state data. The traffic planning engine is configured to determine a target route. The task solving engine and the traffic planning engine each provide an application programming interface (API).

A device and a method for controlling one or more robots. The method includes: for each robot of the one or more robots, determining a labeled Markov decision process for each skill of one or more skills which the robot is able to implement, each labeled Markov decision process having a labeling function which indicates for each state of the labeled Markov decision process, whether one or more control conditions of a plurality of predetermined control conditions is/are satisfied; providing a control mission defined by a mission specification, the mission specification having a time sequence over a subset of the plurality of predetermined control conditions; and controlling the one or more robots to execute the control mission in such a way that the control conditions of states of the one or more robots contained in the mission specification are satisfied.

The invention concerns a method, arrangement and computer program product for distributing processing for a first robot in a cell among more than one processing entities. The arrangement includes a processing entity determining unit that obtains data about current limitations in the processing environment of a prospective processing entity intended to perform a processing task for the first robot, determines, based on the processing environment limitations, whether a performance requirement will be fulfilled or not if the task is performed in the prospective processing entity, and assigns the processing task for processing in the prospective processing entity or in at least one other processing entity based on the determining of whether the performance requirement is fulfilled or not.

A robot cluster scheduling system includes a user layer, an intermediate layer, an application layer, a plug-in layer and a data persistence layer. The intermediate layer includes a processor mapping module and a state acquisition module. The application layer includes a task scheduling module and a traffic scheduling module. The plug-in layer includes a task solving engine and a traffic planning engine. The task solving engine is configured to determine a target robot according to a parameter of a task and state data. The traffic planning engine is configured to determine a target route. The task solving engine and the traffic planning engine each provide an application programming interface (API).

The invention concerns a method, arrangement and computer program product for distributing processing for a first robot in a cell among more than one processing entities. The arrangement includes a processing entity determining unit that obtains data about current limitations in the processing environment of a prospective processing entity intended to perform a processing task for the first robot, determines, based on the processing environment limitations, whether a performance requirement will be fulfilled or not if the task is performed in the prospective processing entity, and assigns the processing task for processing in the prospective processing entity or in at least one other processing entity based on the determining of whether the performance requirement is fulfilled or not.

A method operable by a computing device is provided. The method may include receiving a request for a given task to be performed by a robotic system. The method may also determining one or more subtasks required to perform the given task, where the one or more subtasks include one or more parameters used to define the one or more subtasks. The method may also include determining an arrangement of the one or more subtasks to perform the given task, and providing for display an indication of the one or more undefined parameters for the given task. The method may also include receiving an input defining the one or more undefined parameters for the given task, and executing the one or more subtasks in the determined arrangement and in accordance with the one or more defined parameters to cause the robotic system to perform the given task.

Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot is described comprising a robotic skeleton designed similar to that of a human skeleton to simulate similar movements as performed by living subjects. The movements of the robotic skeleton are resultant due to control signals received by effectors present near or on the robotic skeleton. The robot can be configured to receive sensor data transmitted from a sensor apparatus that periodically gathers the sensor data from a living subject. The robot can then process the sensor data to transmit control signals to the effectors to simulate the actions performed by the living subject and perform a predictive analysis to learn the capability of generating spontaneous and adaptive actions, resulting in an autonomous robot that can adapt to its surroundings.

Methods and systems for robot cloud computing are described. Within examples, cloud-based computing generally refers to networked computer architectures in which application execution and storage may be divided, to some extent, between client and server devices. A robot may be any device that has a computing ability and interacts with its surroundings with an actuation capability (e.g., electromechanical capabilities). A client device may be configured as a robot including various sensors and devices in the forms of modules, and different modules may be added or removed from robot depending on requirements. In some example, a robot may be configured to receive a second device, such as mobile phone, that may be configured to function as an accessory or a brain of the robot. A robot may interact with the cloud to perform any number of actions, such as to share information with other cloud computing devices.

Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot is described comprising a robotic skeleton designed similar to that of a human skeleton to simulate similar movements as performed by living subjects. The movements of the robotic skeleton are resultant due to control signals received by effectors present near or on the robotic skeleton. The robot can be configured to receive sensor data transmitted from a sensor apparatus that periodically gathers the sensor data from a living subject. The robot can then process the sensor data to transmit control signals to the effectors to simulate the actions performed by the living subject and perform a predictive analysis to learn the capability of generating spontaneous and adaptive actions, resulting in an autonomous robot that can adapt to its surroundings.

An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.