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.

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 an NL prompt or query. The LLM module provides a task plan in NL, which can be evaluated for at least one fault or error. If at least one fault or error is identified, the LLM module can be queried to provide a resolution.

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.

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.

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 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.

A lab system identifies a set of steps associated with a protocol for a lab meant to be performed by a robot within the lab using equipment and reagents. The lab system renders, within a user interface, a virtual representation of the lab, a virtual robot, and virtual equipment and reagents. Responsive to operating in a first mode, the lab system simulates the identified set of steps identify virtual positions of the virtual robot within the lab as the virtual robot performs the steps and modifies the virtual representation of the lab to mirror the identified positions of the virtual robot in real-time. Responsive to operating in a second mode, the lab system identifies positions of the robot within the lab as the robot performs the identified set of steps and modifies the virtual representation of the lab to mirror the identified positions of the robot in real-time.

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 identifies a set of steps associated with a protocol for a lab meant to be performed by a robot within the lab using equipment and reagents. The lab system renders, within a user interface, a virtual representation of the lab, a virtual robot, and virtual equipment and reagents. Responsive to operating in a first mode, the lab system simulates the identified set of steps identify virtual positions of the virtual robot within the lab as the virtual robot performs the steps and modifies the virtual representation of the lab to mirror the identified positions of the virtual robot in real-time. Responsive to operating in a second mode, the lab system identifies positions of the robot within the lab as the robot performs the identified set of steps and modifies the virtual representation of the lab to mirror the identified positions of the robot in real-time.