Abstract: According to at least one implementation, a method includes obtaining a query at a wearable device and identifying context associated with the query, the context comprising a set of images captured by the wearable device. The method further includes generating at least one search from the query and the context and obtaining at least one result to the at least one search. The method also generates a response to the query based on at least one result and provides the response to a wearable device user.

Abstract: A system for automating software usage includes an agent configured to automate. The agent is trained on one or more training data sets. The one or more training datasets include one or more of a first training dataset including documents containing text interleaved with images, a second training dataset including text embedded in images, a third training dataset including recorded videos of software usage, a fourth training dataset including portable document format (PDF) documents, a fifth training dataset including recorded videos of software tool usage trajectories, a sixth training dataset including images of open-domain web pages, a seventh training dataset including images of specific-domain web pages, and/or an eighth training dataset including images of agentic trajectories of the agent performing interface automation task workflows.

Abstract: An example method for providing a graphical user interface (GUI) of a computing device includes receiving an input indicating a target pose of the robot, providing for display on the GUI of the computing device a transparent representation of the robot as a preview of the target pose in combination with the textured model of the robot indicating the current state of the robot, generating a boundary illustration on the GUI representative of a limit of a range of motion of the robot, based on the target pose extending the robot beyond the boundary illustration, modifying characteristics of the transparent representation of the robot and of the boundary illustration on the GUI to inform of an invalid pose, and based on the target pose being a valid pose, sending instructions to the robot causing the robot to perform the target pose.

Abstract: A system for providing artificial intelligence agents that automate software usage includes training servers configured to train agents during training, production servers configured to execute the trained agents during inference, a plurality of training datasets, and data flow logic. The data flow logic is configured to, provide, during the training, the agents and the plurality of training datasets to the training servers to cause the training servers to train the agents on the plurality of training datasets and thereby produce the trained agents, configure the production servers with the trained agents for use during the inference, provide, during the inference, prompts issued by clients to the production servers to cause the production servers to translate the prompts into agent calls to the trained agents that in turn cause the trained agents to generate outputs that are responsive to the prompts, and make the outputs available to the clients.

Abstract: A system for automating software usage includes an agent configured to automate. The agent is trained on one or more training data sets. The one or more training datasets include one or more of a first training dataset including documents containing text interleaved with images, a second training dataset including text embedded in images, a third training dataset including recorded videos of software usage, a fourth training dataset including portable document format (PDF) documents, a fifth training dataset including recorded videos of software tool usage trajectories, a sixth training dataset including images of open-domain web pages, a seventh training dataset including images of specific-domain web pages, and/or an eighth training dataset including images of agentic trajectories of the agent performing interface automation task workflows.

Abstract: According to at least one implementation, a method includes obtaining a query at a wearable device and identifying context associated with the query, the context comprising a set of images captured by the wearable device. The method further includes generating at least one search from the query and the context and obtaining at least one result to the at least one search. The method also generates a response to the query based on at least one result and provides the response to a wearable device user.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: A robotics-assisted foundation installation system is provided in which data reporting the X, Y, and Z positions of foundation column tops are sent from a total surveying station to a grid control system. The grid control system receives the data and associates specific data with specific columns in an array—the “grid.” The grid control system compares the actual positions of the columns in the grid to target positions that were determined based on the requirements of the structure to be supported. After determining differences between the actual positions and the target positions, the grid control system sends instructions to column positioning tools associated with the individual columns. Actuators in a column positioning tool are directed by the grid control system to adjust the position of the associated column. Once the live streamed data confirms that each column is in the proper position, the columns are fixed in place.

Abstract: An example computer-implemented method includes receiving, from one or more vision components in an environment, vision data that captures features of the environment, including object features of an object that is located in the environment, and prior to a robot manipulating the object: (i) determining based on the vision data, at least one first adjustment to a programmed trajectory of movement of the robot operating in the environment to perform a task of transporting the object, and (ii) determining based on the object features of the object, at least one second adjustment to the programmed trajectory of movement of the robot operating in the environment to perform the task, and causing the robot to perform the task, in accordance with the at least one first adjustment and the at least one second adjustment to the programmed trajectory of movement of the robot.

Abstract: A robotics-assisted foundation installation system is provided in which data reporting the X, Y, and Z positions of foundation column tops are sent from a total surveying station to a grid control system. The grid control system receives the data and associates specific data with specific columns in an array—the “grid.” The grid control system compares the actual positions of the columns in the grid to target positions that were determined based on the requirements of the structure to be supported. After determining differences between the actual positions and the target positions, the grid control system sends instructions to column positioning tools associated with the individual columns. Actuators in a column positioning tool are directed by the grid control system to adjust the position of the associated column. Once the live streamed data confirms that each column is in the proper position, the columns are fixed in place.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: The present invention provides a compound having the structure: a processing of making the compound; and a process of using the compound as a reagent for the difluoromethoxylation and trifluoromethoxylation of arenes or heteroarenes.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: A robotics-assisted foundation installation system is provided in which data reporting the X, Y, and Z positions of foundation column tops are sent from a total surveying station to a grid control system. The grid control system receives the data and associates specific data with specific columns in an array - the “grid.” The grid control system compares the actual positions of the columns in the grid to target positions that were determined based on the requirements of the structure to be supported. After determining differences between the actual positions and the target positions, the grid control system sends instructions to column positioning tools associated with the individual columns. Actuators in a column positioning tool are directed by the grid control system to adjust the position of the associated column. Once the live streamed data confirms that each column is in the proper position, the columns are fixed in place.

Abstract: An example computer-implemented method includes receiving, from one or more vision components in an environment, vision data that captures features of the environment, including object features of an object that is located in the environment, and prior to a robot manipulating the object: (i) determining based on the vision data, at least one first adjustment to a programmed trajectory of movement of the robot operating in the environment to perform a task of transporting the object, and (ii) determining based on the object features of the object, at least one second adjustment to the programmed trajectory of movement of the robot operating in the environment to perform the task, and causing the robot to perform the task, in accordance with the at least one first adjustment and the at least one second adjustment to the programmed trajectory of movement of the robot.

Abstract: An example method for providing a graphical user interface (GUI) of a computing device includes receiving an input indicating a target pose of the robot, providing for display on the GUI of the computing device a transparent representation of the robot as a preview of the target pose in combination with the textured model of the robot indicating the current state of the robot, generating a boundary illustration on the GUI representative of a limit of a range of motion of the robot, based on the target pose extending the robot beyond the boundary illustration, modifying characteristics of the transparent representation of the robot and of the boundary illustration on the GUI to inform of an invalid pose, and based on the target pose being a valid pose, sending instructions to the robot causing the robot to perform the target pose.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.

Abstract: The present invention provides a compound having the structure: a processing of making the compound; and a process of using the compound as a reagent for the difluoromethoxylation and trifluoromethoxylation of arenes or heteroarenes.

Abstract: Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.