Abstract: Apparatus and methods for arbitration of control signals for robotic devices. A robotic device may comprise an adaptive controller comprising a plurality of predictors configured to provide multiple predicted control signals based on one or more of the teaching input, sensory input, and/or performance. The predicted control signals may be configured to cause two or more actions that may be in conflict with one another and/or utilize a shared resource. An arbitrator may be employed to select one of the actions. The selection process may utilize a WTA, reinforcement, and/or supervisory mechanisms in order to inhibit one or more predicted signals. The arbitrator output may comprise target state information that may be provided to the predictor block. Prior to arbitration, the predicted control signals may be combined with inputs provided by an external control entity in order to reduce learning time.

Abstract: Computerized appliances may be operated by users remotely. In one implementation, a learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.

Abstract: Computerized appliances may be operated by users remotely. A learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.

Abstract: Computerized appliances may be operated by users remotely. In one exemplary implementation, a learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.

Abstract: Computerized appliances may be operated by users remotely. A learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.

Abstract: A robotic device may comprise an adaptive controller configured to learn to predict consequences of robotic device's actions. During training, the controller may receive a copy of the planned and/or executed motor command and sensory information obtained based on the robot's response to the command. The controller may predict sensory outcome based on the command and one or more prior sensory inputs. The predicted sensory outcome may be compared to the actual outcome. Based on a determination that the prediction matches the actual outcome, the training may stop. Upon detecting a discrepancy between the prediction and the actual outcome, the controller may provide a continuation signal configured to indicate that additional training may be utilized. In some classification implementations, the discrepancy signal may be used to indicate occurrence of novel (not yet learned) objects in the sensory input and/or indicate continuation of training to recognize said objects.

Abstract: Apparatus and methods for training of robotic devices. A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. User interface of the remote controller may be reconfigured based on the detected phenotype and/or operational changes.

Abstract: Robotic devices may be operated by users remotely. A learning controller apparatus may detect remote transmissions comprising user control instructions. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The learning apparatus may monitor one or more wavelength (infrared light, radio channel) and detect transmissions from user remote control device to the robot during its operation by the user. The learning apparatus may be configured to develop associations between the detected user remote control instructions and actions of the robot for given context. When a given sensory context occurs, the learning controller may automatically provide control instructions to the robot that may be associates with the given context. The provision of control instructions to the robot by the learning controller may obviate the need for user remote control of the robot thereby enabling autonomous operation by the robot.

Abstract: Robotic devices may be operated by users remotely. A learning controller apparatus may detect remote transmissions comprising user control instructions. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The learning apparatus may monitor one or more wavelength (infrared light, radio channel) and detect transmissions from user remote control device to the robot during its operation by the user. The learning apparatus may be configured to develop associations between the detected user remote control instructions and actions of the robot for given context. When a given sensory context occurs, the learning controller may automatically provide control instructions to the robot that may be associates with the given context. The provision of control instructions to the robot by the learning controller may obviate the need for user remote control of the robot thereby enabling autonomous operation by the robot.

Abstract: Robotic devices may be operated by users remotely. A learning controller apparatus may detect remote transmissions comprising user control instructions. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The learning apparatus may monitor one or more wavelength (infrared light, radio channel) and detect transmissions from user remote control device to the robot during its operation by the user. The learning apparatus may be configured to develop associations between the detected user remote control instructions and actions of the robot for given context. When a given sensory context occurs, the learning controller may automatically provide control instructions to the robot that may be associates with the given context. The provision of control instructions to the robot by the learning controller may obviate the need for user remote control of the robot thereby enabling autonomous operation by the robot.

Abstract: Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.

Abstract: A robotic device may comprise an adaptive controller configured to learn to predict consequences of robotic device's actions. During training, the controller may receive a copy of the planned and/or executed motor command and sensory information obtained based on the robot's response to the command. The controller may predict sensory outcome based on the command and one or more prior sensory inputs. The predicted sensory outcome may be compared to the actual outcome. Based on a determination that the prediction matches the actual outcome, the training may stop. Upon detecting a discrepancy between the prediction and the actual outcome, the controller may provide a continuation signal configured to indicate that additional training may be utilized. In some classification implementations, the discrepancy signal may be used to indicate occurrence of novel (not yet learned) objects in the sensory input and/or indicate continuation of training to recognize said objects.

Abstract: Robotic devices may be trained by a trainer guiding the robot along a target trajectory using physical contact with the robot. The robot may comprise an adaptive controller configured to generate control commands based on one or more of the trainer input, sensory input, and/or performance measure. The trainer may observe task execution by the robot. Responsive to observing a discrepancy between the target behavior and the actual behavior, the trainer may provide a teaching input via a haptic action. The robot may execute the action based on a combination of the internal control signal produced by a learning process of the robot and the training input. The robot may infer the teaching input based on a comparison of a predicted state and actual state of the robot. The robot's learning process may be adjusted in accordance with the teaching input so as to reduce the discrepancy during a subsequent trial.

Abstract: Apparatus and methods for arbitration of control signals for robotic devices. A robotic device may comprise an adaptive controller comprising a plurality of predictors configured to provide multiple predicted control signals based on one or more of the teaching input, sensory input, and/or performance. The predicted control signals may be configured to cause two or more actions that may be in conflict with one another and/or utilize a shared resource. An arbitrator may be employed to select one of the actions. The selection process may utilize a WTA, reinforcement, and/or supervisory mechanisms in order to inhibit one or more predicted signals. The arbitrator output may comprise target state information that may be provided to the predictor block. Prior to arbitration, the predicted control signals may be combined with inputs provided by an external control entity in order to reduce learning time.

Abstract: Apparatus and methods for arbitration of control signals for robotic devices. A robotic device may comprise an adaptive controller comprising a plurality of predictors configured to provide multiple predicted control signals based on one or more of the teaching input, sensory input, and/or performance. The predicted control signals may be configured to cause two or more actions that may be in conflict with one another and/or utilize a shared resource. An arbitrator may be employed to select one of the actions. The selection process may utilize a WTA, reinforcement, and/or supervisory mechanisms in order to inhibit one or more predicted signals. The arbitrator output may comprise target state information that may be provided to the predictor block. Prior to arbitration, the predicted control signals may be combined with inputs provided by an external control entity in order to reduce learning time.

Abstract: A robot may be trained based on cooperation between an operator and a trainer. During training, the operator may control the robot using a plurality of control instructions. The trainer may observe movements of the robot and generate a plurality of control commands, such as gestures, sound and/or light wave modulation. Control instructions may be combined with the trainer commands via a learning process in order to develop an association between the two. During operation, the learning process may generate one or more control instructions based on one or more gesture by the trainer. One or both the trainer or the operator may comprise a human, and/or computerized entity.

Abstract: Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task.

Abstract: A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. The remote controller may comprise multiple control elements configured to activate respective portions of the robot platform. Based on training, the remote controller may configure composite controls configured based two or more of control elements. Activation of a composite control may enable the robot to perform a task.

Abstract: Apparatus and methods for training of robotic devices. A robot may be trained by a user guiding the robot along target trajectory using a control signal. A robot may comprise an adaptive controller. The controller may be configured to generate control commands based on the user guidance, sensory input and a performance measure. A user may interface to the robot via an adaptively configured remote controller. The remote controller may comprise a mobile device, configured by the user in accordance with phenotype and/or operational configuration of the robot. The remote controller may detect changes in the robot phenotype and/or operational configuration. User interface of the remote controller may be reconfigured based on the detected phenotype and/or operational changes.

Abstract: This invention relates to a maximization of the inferences which are derived from data involved in data processing representing asset modes based on applicable state representations. The maximization takes place using a single algorithm to automatically analyze information and infer from electric power turbine configurations.