Abstract: Systems and methods for enhancing task performance and computer readable maps produced by robots using modular sensors is disclosed herein. According to at least one non-limiting exemplary embodiment, robots may perform a first set of tasks, wherein coupling one or more modular sensors to the robots may configure a robot to perform a second set of tasks, the second set of tasks includes the first set of tasks and at least one additional task.

Abstract: Systems and methods for data and confidentiality preservation for feature identification by robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a method for determining if an image depicts a human is disclosed. The determination may be utilized to either (i) censor the face of the human, or (ii) identify features within the image. The determination enhances feature identification by ensuring only uncensored and unobscured images are provided to one or more models for the identification to preserve human confidentiality.

Abstract: Systems and methods for robotic disinfection and sanitation of environments are disclosed herein. The robotic devices disclosed herein may detect and map locations of pathogens within environments. The robotic devices disclosed herein may utilize data from sensor units to sanitize objects using desired methods specified by operators. The robotic devices disclosed herein may sanitize environments comprising people.

Abstract: Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Abstract: Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Abstract: Systems and methods for training neural networks on a cloud server using sensory data collected by plurality of robots is disclosed herein. The model may be derived from one or more trained neural networks, the neural networks being trained using data collected by one or more robots. Advantageously, data collection by robots may enhance consistency, reliability, and quality of data received for use in training one or more neural networks. The model may be utilized by robots, upon sufficient training of the neural networks, such that the robots may identify features within their environments. Advantageously, the model may be trained on a cloud server and utilized by individual robots for use in enhancing autonomy of the robots, wherein the utilization of the model requires significantly fewer computational resources than training of the neural networks to develop the model.

Abstract: Apparatus and methods for training and operating of robotic appliances. Robotic appliance may be operable to clean user premises. The user may train the appliance to perform cleaning operations in constrained areas. The appliance may be configured to clean other area of the premises automatically. The appliance may perform premises exploration and/or determine map of the premises. The appliance may be provided priority information associated with areas of the premises. The appliance may perform cleaning operations in order of the priority. Robotic vacuum cleaner appliance may be configured for safe cable operation wherein the controller may determine one or more potential obstructions (e.g., a cable) along operating trajectory. Upon approaching the cable, the controller may temporarily disable brushing mechanism in order to prevent cable damage.

Abstract: Systems and methods for localizing devices using network signatures and coverage maps measured by robots are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may generate a coverage map based on measurements collected during operation of the robot. The coverage map generated by the robot may be temporally accurate such that a device may be localized within the coverage map based on a received network signature from the device. The network signature comprising a measure of amplitudes of Wi-Fi networks and/or cellular networks at a point within an environment of the coverage map.

Abstract: Systems and methods for detection of features within data collected by a plurality of robots by a centralized server are disclosed herein. According to at least one non-limiting exemplary embodiment, a plurality of robots may be utilized to collect a substantial amount of feature data using one or more sensors coupled thereto, wherein use of the plurality of robots to collect the feature data yields accurate localization of the feature data and consistent acquisition of the feature data. Systems and methods disclosed herein further enable a cloud server to identify a substantial number of features within the acquired feature data for purposes of generating insights. The substantial number of features far exceed a practical number of features of which a single neural network may be trained to identify.

Abstract: Systems, apparatuses, and methods for a distributed network of data collection and insight generation by server are disclosed herein. According to at least one non-limiting exemplary embodiment, the server may be configured to receive data from a network of data sources, receive an application from an application creator, and execute the application based on the data from the network of data sources to generate at least one insight, wherein the network of data sources may comprise mobile robots, stationary devices, IoT (Internet of Things) devices, and/or public data sources. The at least one insight may be utilized by robots to improve efficiency of operation or by humans to gain useful insights to the environment in which the data sources operate.

Abstract: Systems and methods for enhancing task performance and computer readable maps produced by robots using modular sensors is disclosed herein. According to at least one non-limiting exemplary embodiment, robots may perform a first set of tasks, wherein coupling one or more modular sensors to the robots may configure a robot to perform a second set of tasks, the second set of tasks includes the first set of tasks and at least one additional task.

Abstract: Systems and methods for detection of features within data collected by a plurality of robots by a centralized server are disclosed herein. According to at least one non-limiting exemplary embodiment, a plurality of robots may be utilized to collect a substantial amount of feature data using one or more sensors coupled thereto, wherein use of the plurality of robots to collect the feature data yields accurate localization of the feature data and consistent acquisition of the feature data. Systems and methods disclosed herein further enable a cloud server to identify a substantial number of features within the acquired feature data for purposes of generating insights. The substantial number of features far exceed a practical number of features of which a single neural network may be trained to identify.

Abstract: Apparatus and methods for navigation of a robotic device configured to operate in an environment comprising objects and/or persons. Location of objects and/or persons may change prior and/or during operation of the robot. In one embodiment, a bistatic sensor comprises a transmitter and a receiver. The receiver may be spatially displaced from the transmitter. The transmitter may project a pattern on a surface in the direction of robot movement. In one variant, the pattern comprises an encoded portion and an information portion. The information portion may be used to communicate information related to robot movement to one or more persons. The encoded portion may be used to determine presence of one or more object in the path of the robot. The receiver may sample a reflected pattern and compare it with the transmitted pattern. Based on a similarity measure breaching a threshold, indication of object present may be produced.

Abstract: A system for controlling movement of a device comprises at least one processor configured to receive a first input from a sensor upon detection of an obstacle in a first region of the device and a different second input from the sensor upon detection of the object in a different second region of the device and further configured to transmit a first signal to at least one actuator upon receiving the first input from the sensor, the first signal including a strength of first value and transmit a second signal upon receiving the second input from the sensor, the second value being greater than the first value.

Abstract: Apparatus and methods for training and operating of robotic appliances. Robotic appliance may be operable to clean user premises. The user may train the appliance to perform cleaning operations in constrained areas. The appliance may be configured to clean other area of the premises automatically. The appliance may perform premises exploration and/or determine map of the premises. The appliance may be provided priority information associated with areas of the premises. The appliance may perform cleaning operations in order of the priority. Robotic vacuum cleaner appliance may be configured for safe cable operation wherein the controller may determine one or more potential obstructions (e.g., a cable) along operating trajectory. Upon approaching the cable, the controller may temporarily disable brushing mechanism in order to prevent cable damage.

Abstract: Apparatus and methods for training and operating of robotic appliances. Robotic appliance may be operable to clean user premises. The user may train the appliance to perform cleaning operations in constrained areas. The appliance may be configured to clean other area of the premises automatically. The appliance may perform premises exploration and/or determine map of the premises. The appliance may be provided priority information associated with areas of the premises. The appliance may perform cleaning operations in order of the priority. Robotic vacuum cleaner appliance may be configured for safe cable operation wherein the controller may determine one or more potential obstructions (e.g., a cable) along operating trajectory. Upon approaching the cable, the controller may temporarily disable brushing mechanism in order to prevent cable damage.

Abstract: Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Abstract: Apparatus and methods for high-level neuromorphic network description (HLND) framework that may be configured to enable users to define neuromorphic network architectures using a unified and unambiguous representation that is both human-readable and machine-interpretable. The framework may be used to define nodes types, node-to-node connection types, instantiate node instances for different node types, and to generate instances of connection types between these nodes. To facilitate framework usage, the HLND format may provide the flexibility required by computational neuroscientists and, at the same time, provides a user-friendly interface for users with limited experience in modeling neurons. The HLND kernel may comprise an interface to Elementary Network Description (END) that is optimized for efficient representation of neuronal systems in hardware-independent manner and enables seamless translation of HLND model description into hardware instructions for execution by various processing modules.

Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a plurality of predictor apparatus configured to generate motor control output. One predictor may be operable in accordance with a pre-configured process; another predictor may be operable in accordance with a learning process configured based on a teaching signal. An adaptive combiner component may be configured to determine a combined control output controller block may provide control output that may be combined with the predicted control output. The pre-programmed predictor may be configured to operate a robot to perform a task. Based on detection of a context, the controller may adaptively switch to use control output of the learning process to perform the given or another task. User feedback may be utilized during learning.

Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a plurality of predictor apparatus configured to generate motor control output. One predictor may be operable in accordance with a pre-configured process; another predictor may be operable in accordance with a learning process configured based on a teaching signal. An adaptive combiner component may be configured to determine a combined control output controller block may provide control output that may be combined with the predicted control output. The pre-programmed predictor may be configured to operate a robot to perform a task. Based on detection of a context, the controller may adaptively switch to use control output of the learning process to perform the given or another task. User feedback may be utilized during learning.