Abstract: Methods, apparatus, systems, and computer-readable media are provided for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data collected from one or more robots. Historical task and location correlated duration data may, in some implementations, include durations of different tasks performed in different locations by one or more robots in one or more particular environments, and knowledge of such durations may be used to optimize tasks performed by the same or different robots in the future.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data collected from one or more robots. Historical task and location correlated duration data may, in some implementations, include durations of different tasks performed in different locations by one or more robots in one or more particular environments, and knowledge of such durations may be used to optimize tasks performed by the same or different robots in the future.

Abstract: An example method includes determining a plurality of frequency ranges corresponding to a plurality of types of errors, where the plurality of frequency ranges are associated with sounds occurring during operation of a robotic device. The method also includes detecting, based on sensor data from at least one audio sensor of the robotic device, a sound during a given operation of the robotic device. The method also includes determining that a frequency of the detected sound is within a particular frequency range of the plurality of frequency ranges. Based on the frequency being within the particular frequency range, the method also includes determining a type of error of the plurality of types of errors corresponding to the particular frequency range. The method also includes providing an output signal indicating an error of the determined type.

Abstract: An example method includes determining an expected sound profile corresponding to a given task for a robotic device. The method further includes detecting a sound profile during execution of the given task by the robotic device. The method also includes determining one or more differences in amplitude for at least one frequency range between the detected sound profile and the expected sound profile corresponding to the given task for the robotic device. In response to determining the one or more differences in amplitude for the at least one frequency range between the detected sound profile and the expected sound profile, the method additionally includes identifying at least one component of the robotic device associated with the detected sound profile during execution of the given task. The method further includes adjusting control data for the at least one component of the robotic device.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data collected from one or more robots. Historical task and location correlated duration data may, in some implementations, include durations of different tasks performed in different locations by one or more robots in one or more particular environments, and knowledge of such durations may be used to optimize tasks performed by the same or different robots in the future.

Abstract: An example method includes determining an expected sound profile corresponding to a given task for a robotic device. The method further includes detecting a sound profile during execution of the given task by the robotic device. The method also includes determining one or more differences in amplitude for at least one frequency range between the detected sound profile and the expected sound profile corresponding to the given task for the robotic device. In response to determining the one or more differences in amplitude for the at least one frequency range between the detected sound profile and the expected sound profile, the method additionally includes identifying at least one component of the robotic device associated with the detected sound profile during execution of the given task. The method further includes adjusting control data for the at least one component of the robotic device.

Abstract: An example method includes determining an expected sound profile corresponding to a given task for a robotic device. The method further includes detecting a sound profile during execution of the given task by the robotic device. The method also includes determining one or more differences in amplitude for at least one frequency range between the detected sound profile and the expected sound profile corresponding to the given task for the robotic device. In response to determining the one or more differences in amplitude for the at least one frequency range between the detected sound profile and the expected sound profile, the method additionally includes identifying at least one component of the robotic device associated with the detected sound profile during execution of the given task. The method further includes adjusting control data for the at least one component of the robotic device.

Abstract: Embodiments of a dialog system that employs a corpus-based approach to generate responses based on a given number of semantic constraint-value pairs are described. The system makes full use of the data from the user input to produce dialog system responses in combination with a template generator. The system primarily utilizes constraint values in order to realize efficiencies based on the more frequent tasks performed in real dialog systems although rhetorical or discourse aspects of the dialog could also be included in a similar way, that is, labeling the data with such information and performing a training process. The benefits of this system include higher quality user-aligned responses, broader coverage, faster response time, and shorter development cycles.

Abstract: Embodiments of a dialog system that employs a corpus-based approach to generate responses based on a given number of semantic constraint-value pairs are described. The system makes full use of the data from the user input to produce dialog system responses in combination with a template generator. The system primarily utilizes constraint values in order to realize efficiencies based on the more frequent tasks performed in real dialog systems although rhetorical or discourse aspects of the dialog could also be included in a similar way, that is, labeling the data with such information and performing a training process. The benefits of this system include higher quality user-aligned responses, broader coverage, faster response time, and shorter development cycles.