Abstract: Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.

Abstract: Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

Abstract: Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

Abstract: Systems, methods, and computer program products for managing and populating environment models are described. An environment model is accessed which represents an environment, and the environment model is populated with instances of object models. Locations where the instances of object models should be positioned in the environment model are identified, by determining where in the environment model a respective size of each instance when viewed from a vantage point at the environment model matches a size of the object represented by the respective instance when viewed from a corresponding vantage point at the environment.

Abstract: 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. An NL response 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.

Abstract: Robots, systems, methods, and computer program products for training and operating (semi-)autonomous robots to complete work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-action that the robot is trained to autonomously perform. A work objective is analyzed to determine a sequence of reusable work primitives that complete the work objective, and the robot executes the sequence to complete the work objective. A robot can be deployed with access to an appropriate library of reusable work primitives, based on expectations for the robot. The robot is trained to perform reusable work primitives in multiple libraries, by generating control instructions which cause the robot to perform each reusable work primitive.

Abstract: Robots, systems, methods, and computer program products for training and operating (semi-)autonomous robots to complete work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-action that the robot is trained to autonomously perform. A work objective is analyzed to determine a sequence of reusable work primitives that complete the work objective, and the robot executes the sequence to complete the work objective. A robot can be deployed with access to an appropriate library of reusable work primitives, based on expectations for the robot. The robot is trained to perform reusable work primitives in multiple libraries, by generating control instructions which cause the robot to perform each reusable work primitive.

Abstract: Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.

Abstract: Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.

Abstract: Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.

Abstract: A method of operation of a robot includes determining a set of candidate actions to be performed by the robot based on an objective. A level of autonomy of the robot is determined from a control model associated with the robot. A subset of candidate actions for which the level of autonomy of the robot is below a threshold level of autonomy is determined from the set of candidate actions. The robot receives a set of instructions for at least one candidate action in the subset of candidate actions from a tele-operation system. The robot executes the set of instructions and updates the control model based on a result of executing the set of instructions.

Abstract: Robots, methods, and computer program products for training and operating (semi-) autonomous robots to complete multiple different work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. A robot can be deployed with an appropriate stored library (or access to an appropriate library) of reusable work primitives, based on what the robot is expected to do, or what service category or role the robot will operate in.

Abstract: Robots, methods, and computer program products for training and operating (semi-) autonomous robots to complete multiple different work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. A robot can be deployed with an appropriate stored library (or access to an appropriate library) of reusable work primitives, based on what the robot is expected to do, or what service category or role the robot will operate in.

Abstract: Robots, methods, and computer program products for training and operating (semi-) autonomous robots to complete multiple different work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. A robot can be deployed with an appropriate stored library (or access to an appropriate library) of reusable work primitives, based on what the robot is expected to do, or what service category or role the robot will operate in.

Abstract: Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.

Abstract: The present disclosure describes robots and tele-operation systems where a select control paradigm is selected from a plurality of control paradigms based on an identity of an operator, role of an operator, or expected tasks to be performed by the robot. Generic robots can be operated in accordance with any of the plurality of control paradigms, such that any of said generic robots can serve a role or act as an assistant to an operator by selection of an appropriate control paradigm. Control paradigms can be operator specific, or specific to a faction or role which an operator fits in, or specific to a set of tasks to be performed by the robot. The present disclosure also describes feedback mechanisms by which a robot or tele-operator system receive operator feedback and update a control paradigm, to gradually improve the control paradigm over time.

Abstract: The present disclosure describes robots and tele-operation systems where a select control paradigm is selected from a plurality of control paradigms based on an identity of an operator, role of an operator, or expected tasks to be performed by the robot. Generic robots can be operated in accordance with any of the plurality of control paradigms, such that any of said generic robots can serve a role or act as an assistant to an operator by selection of an appropriate control paradigm. Control paradigms can be operator specific, or specific to a faction or role which an operator fits in, or specific to a set of tasks to be performed by the robot. The present disclosure also describes feedback mechanisms by which a robot or tele-operator system receive operator feedback and update a control paradigm, to gradually improve the control paradigm over time.

Abstract: The present disclosure describes robots and tele-operation systems where a select control paradigm is selected from a plurality of control paradigms based on an identity of an operator, role of an operator, or expected tasks to be performed by the robot. Generic robots can be operated in accordance with any of the plurality of control paradigms, such that any of said generic robots can serve a role or act as an assistant to an operator by selection of an appropriate control paradigm. Control paradigms can be operator specific, or specific to a faction or role which an operator fits in, or specific to a set of tasks to be performed by the robot. The present disclosure also describes feedback mechanisms by which a robot or tele-operator system receive operator feedback and update a control paradigm, to gradually improve the control paradigm over time.

Abstract: In a method of operation of a robot, the robot identifies a set of candidate actions that may be performed by the robot, and collects, for each candidate action of the set of candidate actions, a respective set of ancillary data. The robot transmits a request for instructions to a tele-operation system that is communicatively coupled to the robot. The request for instructions includes each candidate action and each respective set of ancillary data. The robot receives, and executes, the instructions from the tele-operation system. The robot updates a control model, based at least in part on each candidate action, each respective set of ancillary data, and the instructions, to increase a level of autonomy of the robot. The robot may transmit the request for instructions to the tele-operation system in response to determining the robot is unable to select a candidate action to perform in furtherance of an objective.