Abstract: A robot is provided having a kinematic chain comprising a plurality of joints and links, including a root joint connected to a robot pedestal, and at least one end effector. A plurality of actuators are fixedly mounted on the robot pedestal. A plurality of tendons is connected to a corresponding plurality of actuation points on the kinematic chain and to actuators in the plurality of actuators, arranged to translate actuator position and force to actuation points for tendon-driven joints on the kinematic chain with losses in precision due to variability of tendons in the plurality of tendons. A controller operates the kinematic chain to perform a task. The controller is configured to generate actuator command data in dependence on the actuator states and image data in a manner that compensates for the losses in precision in the tendon-driven mechanisms.

Abstract: An integrated security system integrates broadband and mobile access and control with conventional security systems and premise devices to provide a tri-mode security network that with remote connectivity and access. The integrated security system includes a touchscreen providing security keypad functionality as well as content management and presentation, and is used as a security system interface and an interface for interacting with a network. The integrated security system delivers remote premise monitoring and control functionality to conventional monitored premise protection and complements existing premise protection equipment. The integrated security system integrates into the premise network and couples wirelessly with the conventional security panel, enabling broadband access to premise security systems. Automation devices can be added, enabling users to remotely see live video or pictures and control home devices via a personal web portal, mobile phone, or other client device.

Abstract: A robot is provided having a kinematic chain comprising a plurality of joints and links, including a root joint connected to a robot pedestal, and at least one end effector. A plurality of actuators are fixedly mounted on the robot pedestal. A plurality of tendons is connected to a corresponding plurality of actuation points on the kinematic chain and to actuators in the plurality of actuators, arranged to translate actuator position and force to actuation points for tendon-driven joints on the kinematic chain with losses in precision due to variability of tendons in the plurality of tendons. A controller operates the kinematic chain to perform a task. The controller is configured to generate actuator command data in dependence on the actuator states and image data in a manner that compensates for the losses in precision in the tendon-driven mechanisms.

Abstract: Provided is a process that includes obtaining data indicative of state of a dynamic mechanical system and an environment of the dynamic mechanical system, the data comprising a plurality of channels of data from a plurality of different sensors including a plurality of cameras and other sensors indicative of state of actuators of the dynamic mechanical system; forming a training set from the obtained data by segmenting the data by time and grouping segments from the different channels by time to form units of training data that span different channels among the plurality of channels; training a metric learning model to encode inputs corresponding to the plurality of channels as vectors in an embedding space with self-supervised learning based on the training set; and using the trained metric learning model to control the dynamic mechanical system or another dynamic mechanical system.

Abstract: Provided is a robot that includes: a first sensor having a first output and configured to sense state of a robot or an environment of the robot; a first hardware machine-learning accelerator coupled to the first output of the first sensor and configured to transform information sensed by the first sensor into a first latent-space representation; a second sensor having a second output and configured to sense state of the robot or the environment of the robot; a second hardware machine-learning accelerator configured to transform information sensed by the second sensor into a second latent-space representation; and a processor configured to control the robot based on both the first latent-space representation and the second latent-space representation.

Abstract: Methods and systems for managing a security system are described. A security server located external to a premises may be in communication with a security system and a gateway device located at the premises. A status signal may be sent to the security server. The status signal may be initiated based on an alarm event. If the security server fails to detect a message of the status signal, the security server may send one or messages indicating the failure.

Abstract: Methods and systems for using a teleoperation system to train a robot to perform tasks using machine learning are described herein. A teleoperation system may be used to record actions of a robot as used by a human teleoperator. The teleoperation system may provide a teleoperator insight into the state of the robot and may provide feedback to the teleoperator allowing the teleoperator to feel what the robot is feeling. For example, sensor information from the robot may be sent to the teleoperation system and output to the teleoperator in various forms including vibrations, video, visual cues, or sound. The teleoperation system may output visual guides to the teleoperator so that the teleoperator may know how to control the robot to complete a task in a desired manner.

Abstract: Provided is a robot that includes: a first sensor having a first output and configured to sense state of a robot or an environment of the robot; a first hardware machine-learning accelerator coupled to the first output of the first sensor and configured to transform information sensed by the first sensor into a first latent-space representation; a second sensor having a second output and configured to sense state of the robot or the environment of the robot; a second hardware machine-learning accelerator configured to transform information sensed by the second sensor into a second latent-space representation; and a processor configured to control the robot based on both the first latent-space representation and the second latent-space representation.

Abstract: Provided is a process that includes obtaining data indicative of state of a dynamic mechanical system and an environment of the dynamic mechanical system, the data comprising a plurality of channels of data from a plurality of different sensors including a plurality of cameras and other sensors indicative of state of actuators of the dynamic mechanical system; forming a training set from the obtained data by segmenting the data by time and grouping segments from the different channels by time to form units of training data that span different channels among the plurality of channels; training a metric learning model to encode inputs corresponding to the plurality of channels as vectors in an embedding space with self-supervised learning based on the training set; and using the trained metric learning model to control the dynamic mechanical system or another dynamic mechanical system.

Abstract: Methods and systems for using a teleoperation system to train a robot to perform tasks using machine learning are described herein. A teleoperation system may be used to record actions of a robot as used by a human teleoperator. The teleoperation system may provide a teleoperator insight into the state of the robot and may provide feedback to the teleoperator allowing the teleoperator to feel what the robot is feeling. For example, sensor information from the robot may be sent to the teleoperation system and output to the teleoperator in various forms including vibrations, video, visual cues, or sound. The teleoperation system may output visual guides to the teleoperator so that the teleoperator may know how to control the robot to complete a task in a desired manner.

Abstract: Methods and systems for monitoring a premises are described. A computing device may receive data from a premises management system. The computing device determines one or more actions to perform by analyzing the data. The computing device may determine to output, via a user interface, an indication associated with the data. A user may request video associated with the data.

Abstract: Provided is a robot that includes: a first sensor having a first output and configured to sense state of a robot or an environment of the robot; a first hardware machine-learning accelerator coupled to the first output of the first sensor and configured to transform information sensed by the first sensor into a first latent-space representation; a second sensor having a second output and configured to sense state of the robot or the environment of the robot; a second hardware machine-learning accelerator configured to transform information sensed by the second sensor into a second latent-space representation; and a processor configured to control the robot based on both the first latent-space representation and the second latent-space representation.

Abstract: Provided is a process that includes obtaining data indicative of state of a dynamic mechanical system and an environment of the dynamic mechanical system, the data comprising a plurality of channels of data from a plurality of different sensors including a plurality of cameras and other sensors indicative of state of actuators of the dynamic mechanical system; forming a training set from the obtained data by segmenting the data by time and grouping segments from the different channels by time to form units of training data that span different channels among the plurality of channels; training a metric learning model to encode inputs corresponding to the plurality of channels as vectors in an embedding space with self-supervised learning based on the training set; and using the trained metric learning model to control the dynamic mechanical system or another dynamic mechanical system.

Abstract: A robot system includes a first computing device and a second computing device. The first computing device is configured to control operation of the robot based on data flows received from a plurality of sensors of the robot and the second computing system is configured to receive and process at least some of the data flows concurrently while the first computing system controls operation of the robot.

Abstract: Provided is a distributed robot management system, including: a first fleet of robots at a first facility; and a robot management server system remote from the first facility and communicatively coupled with the first fleet of robots via a network, wherein the robot management server system is configured to: provide configuration information to the first fleet of robots, maintain a remote representation of state of robots in the first fleet of robots, receive and store data from the first fleet of robots, and provide computing resources by which robots in the first fleet of robots are trained.

Abstract: Provided is a robot that includes: a first sensor having a first output and configured to sense state of a robot or an environment of the robot; a first hardware machine-learning accelerator coupled to the first output of the first sensor and configured to transform information sensed by the first sensor into a first latent-space representation; a second sensor having a second output and configured to sense state of the robot or the environment of the robot; a second hardware machine-learning accelerator configured to transform information sensed by the second sensor into a second latent-space representation; and a processor configured to control the robot based on both the first latent-space representation and the second latent-space representation.

Abstract: Methods and systems for managing a security system are described. A security server located external to a premises may be in communication with a security system and a gateway device located at the premises. A status signal may be sent to the security server. The status signal may be initiated based on an alarm event. If the security server fails to detect a message of the status signal, the security server may send one or messages indicating the failure.

Abstract: Methods and systems for managing a security system are described. A security server located external to a premises may be in communication with a security system and a gateway device located at the premises. A status signal may be sent to the security server. The status signal may be initiated based on an alarm event. If the security server fails to detect a message of the status signal, the security server may send one or messages indicating the failure.

Abstract: Methods and systems for monitoring a premises are described. A computing device may receive data from a premises management system. The computing device determines one or more actions to perform by analyzing the data. The computing device may determine to output, via a user interface, an indication associated with the data. A user may request video associated with the data.

Abstract: An integrated security system integrates broadband and mobile access and control with conventional security systems and premise devices to provide a tri-mode security network that with remote connectivity and access. The integrated security system includes a touchscreen providing security keypad functionality as well as content management and presentation, and is used as a security system interface and an interface for interacting with a network. The integrated security system delivers remote premise monitoring and control functionality to conventional monitored premise protection and complements existing premise protection equipment. The integrated security system integrates into the premise network and couples wirelessly with the conventional security panel, enabling broadband access to premise security systems. Automation devices can be added, enabling users to remotely see live video or pictures and control home devices via a personal web portal, mobile phone, or other client device.