Abstract: A dynamic representation of a robot in an environment is produced, one or more observer agent collects data, and respective values of one or more metrics for the robot are computed based at least in part on the collected data. Tasks for the robot to perform are generated. Ratings and challenge questions are generated. A server may produce a user interface and a value of a metric based on collected observer data.

Abstract: Systems, devices, articles, and methods are illustrated and described herein. A method of operation in a robotic system including a processor, a first device, and a second device involves receiving, by the processor, a training set including a first plurality of positions in a first configuration space that represents physical configurations of the first device, a second plurality of positions in a second configuration space that represents physical configurations of the second device, and information that represents pairs of positions. A representative pair includes a first representative position in the first configuration space and a second representative position in the second configuration space. The method involves creating, by the processor, from the training set, information that represents a map between a first run-time position in the first configuration space, and a second run-time position in the second configuration space, and returning, by the processor, the information that represents the map.

Abstract: Robotic apparatus employ a large variety of resources to operate. A robotic apparatus seeks out sources of energy, computational capacity, shelter, communications, and/or other resources to preserve or renew its energy stores, computational resources, or physical integrity and/or to receive further guidance or direction or to report collected or sensed data or information. A robotic apparatus can determine the existence of a resource deficiency or projected resource deficiency, assess a ranking of such, identify one or more remedial actions, and execute the remedial action(s). A robotic apparatus can assess a ranking of a resource deficiency or projected resource deficiency based on a value of the resource, a severity of need or urgency for the resource, and ability to obtain or replenish the source.

Abstract: A dynamic representation of a robot in an environment is produced, one or more observer agent collects data, and respective values of one or more metrics for the robot are computed based at least in part on the collected data. Tasks for the robot to perform are generated. Ratings and challenge questions are generated. A server may produce a user interface and a value of a metric based on collected observer data.

Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.

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: A method of deriving autonomous control information involves receiving one or more sets of associated environment sensor information and device control instructions. Each set of associated environment sensor information and device control instructions includes environment sensor information representing an environment associated with an operator controllable device and associated device control instructions configured to cause the operator controllable device to simulate at least one action taken by at least one operator experiencing a representation of the environment generated from the environment sensor information.

Abstract: A method of deriving autonomous control information involves receiving one or more sets of associated environment sensor information and device control instructions. Each set of associated environment sensor information and device control instructions includes environment sensor information representing an environment associated with an operator controllable device and associated device control instructions configured to cause the operator controllable device to simulate at least one action taken by at least one operator experiencing a representation of the environment generated from the environment sensor information.

Abstract: A user interface device includes a frame, rigid body in rotary engagement with the frame, a plurality of force sensors, which in response to force acting on the frame produces information that represents a first force component in a first direction with respect to the frame, and a second force component in a second direction with respect to the frame; and an angle sensor, which in response to torque applied to the rigid body produces information that represents rotary movement of the rigid body with respect to the frame about an axis extending vertically through the rigid body. The rigid body can be sized and dimensioned to accommodate one or more feet of a user. The user interface device advantageously combines or mixes isometric and isotonic control input or sensors.

Abstract: Substantially as described and illustrated herein including devices, methods of operation for the systems or devices, articles of manufacture including stores processor-executable instructions, and a system including a robot. The system includes at least one processor. The system may further include a nontransitory processor-readable storage device communicatively coupled to at least one processor and which stores processor-executable instructions which, when executed by the at least one processor, cause the at least one processor to composite environment information that represents an environment and virtual item information that represents the virtual item to produce composited information, present to an agent the composited information, and receive action information that represents an action for the robot to perform via the output system.

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: Systems, devices, articles, and methods are illustrated and described herein. A method of operation in a robotic system including a processor, a first device, and a second device involves receiving, by the processor, a training set including a first plurality of positions in a first configuration space that represents physical configurations of the first device, a second plurality of positions in a second configuration space that represents physical configurations of the second device, and information that represents pairs of positions. A representative pair includes a first representative position in the first configuration space and a second representative position in the second configuration space. The method involves creating, by the processor, from the training set, information that represents a map between a first run-time position in the first configuration space, and a second run-time position in the second configuration space, and returning, by the processor, the information that represents the map.

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: A user interface device includes a frame, rigid body in rotary engagement with the frame, a plurality of force sensors, which in response to force acting on the frame produces information that represents a first force component in a first direction with respect to the frame, and a second force component in a second direction with respect to the frame; and an angle sensor, which in response to torque applied to the rigid body produces information that represents rotary movement of the rigid body with respect to the frame about an axis extending vertically through the rigid body. The rigid body can be sized and dimensioned to accommodate one or more feet of a user. The user interface device advantageously combines or mixes isometric and isotonic control input or sensors.

Abstract: A dynamic representation of a robot in an environment is produced, one or more observer agent collects data, and respective values of one or more metrics for the robot are computed based at least in part on the collected data. Tasks for the robot to perform are generated. Ratings and challenge questions are generated. A server may produce a user interface and a value of a metric based on collected observer data.

Abstract: Robotic apparatus employ a large variety of resources to operate. A robotic apparatus seeks out sources of energy, computational capacity, shelter, communications, and/or other resources to preserve or renew its energy stores, computational resources, or physical integrity and/or to receive further guidance or direction or to report collected or sensed data or information. A robotic apparatus can determine the existence of a resource deficiency or projected resource deficiency, assess a ranking of such, identify one or more remedial actions, and execute the remedial action(s). A robotic apparatus can assess a ranking of a resource deficiency or projected resource deficiency based on a value of the resource, a severity of need or urgency for the resource, and ability to obtain or replenish the source.

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: A method of deriving autonomous control information involves receiving one or more sets of associated environment sensor information and device control instructions. Each set of associated environment sensor information and device control instructions includes environment sensor information representing an environment associated with an operator controllable device and associated device control instructions configured to cause the operator controllable device to simulate at least one action taken by at least one operator experiencing a representation of the environment generated from the environment sensor information.

Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.

Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.