Abstract: Systems, apparatuses, and methods for cost evaluation and motion planning for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a method for producing and evaluating a continuous and differentiable total cost as a function of all available motion commands is disclosed and may be utilized in conjunction with a gradient descent to determine a minimum cost motion command corresponding to an optimal motion for a robotic device to execute in accordance with a target trajectory and obstacle avoidance.

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, apparatuses, and methods for bias determination and value calculation of parameters of a robot are disclosed herein. According to at least one exemplary embodiment, a bias in a navigation parameter may be determined based on a bias in one or more measurement units, wherein a navigation parameter may be a parameter useful to a robot to recreate a route such as, for example, velocity and the bias may be accounted for to more accurately recreate the route and generate accurate maps of an environment.

Abstract: Systems and methods for detecting blind spots using a robotic apparatus are disclosed herein. According to at least one exemplary embodiment, a robot may utilize a plurality of virtual robots or representations to determine intersection points between extended measurements from the robot and virtual measurements from a respective one of the virtual robot or representation to determine blind spots. The robot may additionally consider locations of the blind spots while navigating a route to enhance safety, wherein the robot may perform an action to alert nearby humans upon navigating near a blind spot along the route.

Abstract: Systems, apparatuses, and methods for dynamic filtering of high intensity broadband electromagnetic waves in image data from a sensor of a robot are disclosed herein. According to at least one non-limiting exemplary embodiment, sunlight or light emitted from nearby fluorescent lamps may cause a robot to generate false positives of objects nearby the robot as the light may be of high intensity and large bandwidth. These false positives may cause a robot to get stuck or navigate without use of a camera sensor, which may be unsafe.

Abstract: Systems, apparatuses, and methods for reducing network bandwidth usage by a fleet of robots. According to at least one non-limiting exemplary embodiment, robots coupled to a server collect and produce a substantial amount of data, only a portion of that data being useful for operators to monitor behavior of the robot. The present disclosure provides for, inter alia, optimized systems, apparatuses, and methods for operators to extract the useful data using only reduced bandwidth of cellular LTE networks or Wi-Fi networks.

Abstract: Systems and methods for initializing a robot to autonomously travel a route are disclosed. In some exemplary implementations, a robot can detect an initialization object and then determine its position relative to that initialization object. The robot can then learn a route by user demonstration, where the robot associates actions along that route with positions relative to the initialization object. The robot can later detect the initialization object again and determine its position relative to that initialization object. The robot can then autonomously navigate the learned route, performing actions associated with positions relative to the initialization object.

Abstract: Systems and methods for robotic path planning are disclosed. In some implementations of the present disclosure, a robot can generate a cost map associated with an environment of the robot. The cost map can comprise a plurality of pixels each corresponding to a location in the environment, where each pixel can have an associated cost. The robot can further generate a plurality of masks having projected path portions for the travel of the robot within the environment, where each mask comprises a plurality of mask pixels that correspond to locations in the environment. The robot can then determine a mask cost associated with each mask based at least in part on the cost map and select a mask based at least in part on the mask cost. Based on the projected path portions within the selected mask, the robot can navigate a space.

Abstract: Systems and methods assisting a robotic apparatus are disclosed. In some exemplary implementations, a robot can encounter situations where the robot cannot proceed and/or does not know with a high degree of certainty it can proceed. Accordingly, the robot can determine that it has encountered an error and/or assist event. In some exemplary implementations, the robot can receive assistance from an operator and/or attempt to resolve the issue itself. In some cases, the robot can be configured to delay actions in order to allow resolution of the error and/or assist event.

Abstract: Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.

Abstract: Systems, apparatuses, and methods for rapid machine learning for floor segmentation for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robotic system is disclosed. The robotic system may comprise a neural network embodied therein capable of learning associations between color values of pixels and corresponding classifications of those pixels, wherein neural network is trained initially to identify floor and non-floor pixels within images. A user input may be provided to the neural network to further configure the neural network to be able to identify navigable floors and unnavigable floors unique to an environment without a need for additional annotated training images specific to the environment.

Abstract: Systems and methods for remote operating and/or monitoring of a robot are disclosed. In some exemplary implementations, a robot can be communicatively coupled to a remote network. The remote network can send and receive signals with the robot. In some exemplary implementations, the remote network can receive sensor data from the robot, allowing the remote network to determine the context of the robot. In this way, the remote network can respond to assistance requests and also provide operating commands to the robot.

Abstract: Systems and methods for detecting an escalator in a surrounding environment by a robotic apparatus are disclosed herein. According to at least one exemplary embodiment, an escalator may be determined based on an escalator detection parameter being met. The escalator detection parameter my further require detection of two side walls separated by a distance equal to a width of an escalator and detection of a depreciation in a floor equal to that observed between a stationary portion and a moving first step of an escalator.

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, apparatuses, and methods for bias determination and value calculation of parameters of a robot are disclosed herein. According to at least one exemplary embodiment, a bias in a navigation parameter may be determined based on a bias in one or more measurement units, wherein a navigation parameter may be a parameter useful to a robot to recreate a route such as, for example, velocity and the bias may be accounted for to more accurately recreate the route and generate accurate maps of an environment.

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 robotic mapping are disclosed. In some example implementations, an automated device can travel in an environment. From travelling in the environment, the automated device can create a graph comprising a plurality of nodes, wherein each node corresponds to a scan taken by one or more sensors of the automated device at a location in the environment. In some example embodiments, the automated device can reevaluate its travel along a desired path if it encounters objects or obstructions along its path, whether those objects or obstructions are present in the front, rare or side of the automated device. In some example embodiments, the automated device uses a timestamp methodology to maneuver around its environment that provides faster processing and less usage of memory space.

Abstract: Apparatus and methods for training and operating of robotic devices. Robotic controller may comprise a predictor apparatus configured to generate motor control output. The predictor may be operable in accordance with a learning process based on a teaching signal comprising the control output. An adaptive controller block may provide control output that may be combined with the predicted control output. The predictor learning process may be configured to learn the combined control signal. Predictor training may comprise a plurality of trials. During initial trial, the control output may be capable of causing a robot to perform a task. During intermediate trials, individual contributions from the controller block and the predictor may be inadequate for the task. Upon learning, the control knowledge may be transferred to the predictor so as to enable task execution in absence of subsequent inputs from the controller. Control output and/or predictor output may comprise multi-channel signals.

Abstract: Systems and methods for a universal connection interface between a robot and a plurality of modular attachments are disclosed. The connection interface includes a data connection and a dynamic amplifier configured to adjust output of at least one electromechanically coupled mechanical output; and a processor configured to control gain of the dynamic amplifier.

Abstract: Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.