A method for generating a robot control scenario includes generating a judgement frame by a computational means, storing the judgement frame in a storage means, and displaying the judgement frame by receiving a situation cognition frame and an expression frame; generating a stage by the computation means, storing the stage in the storage means, and displaying the stage by selecting at least one judgement frame and at least one transition frame; and connecting a transition frame of one stage to another stage in at least two stages by the computation means, storing the connected stages in the storage means, and displaying the connected stages. According to the present invention, a robot exists within a close distance to people in a human living space, and can recognize a surrounding situation and information provided by the people and provide a service that meets various requests desired by the people.

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters, environment details, and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. The NL query may include a request for one or more work objectives from the LLM, such as What can I do here?, thereby establishing a form of agency by which the robot system may identify activities to perform without operator intervention. The LLM may also be queried to convert each work objective into a task plan providing a sequence of steps that the robot system may execute to complete the work objective. Optionally, the robot system may communicate with an operator to determine whether or not to execute a task plan.

A lab system accesses a first protocol for performance by a first robot in a first lab. The first protocol includes a set of steps, each associated with an operation, reagent, and equipment. For each of one or more steps, the lab system modifies the step by: (1) identifying one or more replacement operations that achieve an equivalent or substantially similar result as a performance of the operation, (2) identifying replacement equipment that operates substantially similarly to the equipment, and/or (3) identifying one or more replacement reagents that, when substituted for the reagent, do not substantially affect the performance of the step. The lab system generates a modified protocol by replacing one or more of the set of steps with the modified steps. The lab system selects a second lab including a second and configures the second robot to perform the modified protocol in the second lab.

Robot control systems, methods, control modules, and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters, environment details, and/or instruction sets may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. An NL response from the LLM may then be converted into a task plan. A task plan that successfully completes a first instance of a work objective may be parameterized and re-used to complete a second instance of the work objective. Parameterization of a task plan may include replacing one or more nouns/objects in the NL task plan with variables, while optionally preserving one or more verbs/actions in the NL task plan.

The present disclosure provides a general purpose operating system (GPROS) that shows particular usefulness in the robotics and automation fields. The operating system provides individual services and the combination and interconnections of such services using built-in service extensions, built-in completely configurable generic services, and ways to plug in additional service extensions to yield a comprehensive and cohesive framework for developing, configuring, assembling, constructing, deploying, and managing robotics and/or automation applications. The disclosure includes GPROS extensions and features directed to use as an autonomous vehicle operating system. The vehicle controlled by appropriate versions of the GPROS can include unmanned ground vehicle (UGV) applications such as a driverless or self-driving car. The vehicle can likewise or instead include an unmanned aerial vehicle (UAV) such as a helicopter or drone. In cases, the vehicle can include an unmanned underwater vehicle (UUV), such as a submarine or other submersible.

The invention relates to a method of controlling an industrial machine, the industrial machine comprising a control unit, e.g. a PLC, and at least one actuator and/or sensor which is controlled by the control unit, wherein a Language Model is provided, a Language Model Interface is provided, a Context Information Library is provided, which stores information on the industrial machine, particularly commands executable by the industrial machine, wherein the Language Model Interface provides information on the industrial machine from the Context Information Library to the Language Model, the Language Model sends commands to be executed by the industrial machine to the Language Model Interface, the Language Model Interface translates the commands received from the Language Model into machine commands for the control unit.

Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via a recursive sequence of NL prompts or queries. Corresponding NL responses from the LLM may then be converted into robot control parameters and/or instructions. In this way, an LLM may be leveraged by the robot control system to enhance the autonomy of various operations and/or functions, including without limitation task planning, motion planning, human interaction, and/or reasoning about the environment.

A robotic system is contemplated. The robotic system comprises a robot comprising a camera, a microphone, memory, and a controller that is configured to receive a natural language command for performing an action within a real world environment, parse the natural language command, categorize the action as being associated with guidance for performing the action, receive the guidance for performing the action, the guidance including a motion applied to at least one portion of the robot within the real world environment for performing the action, and store, in the memory, the natural language command in correlation with the motion that is applied to the at least one portion of the robot.

Provided is a teaching device whereby it is possible to avoid twisting a fiber connected to a galvano scanner beyond an allowable range. A teaching device for teaching a robot in a laser machining system, wherein the teaching device includes a path determination unit for determining a motion path for a robot on the basis the positions of a plurality of machining points set on an object; a simulation execution unit for executing a robot motion simulation in accordance with the determined motion path; a torsion amount evaluation unit for ascertaining the amount of torsion of an optical fiber by simulating the behavior of the optical fiber in accordance with the movement of the robot according to the motion simulation, and evaluating the torsion amount by comparing the torsion amount and a prescribed allowable range; and a robot orientation changing unit for changing the orientation of the robot so as to reduce the torsion amount for motion of the robot in which the torsion amount exceeds the prescribed allowable range.