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: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.

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.

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: Apparatus and methods for training and controlling of, for instance, robotic devices. In one implementation, a robot may be trained by a user using supervised learning. The user may be unable to control all degrees of freedom of the robot simultaneously. The user may interface to the robot via a control apparatus configured to select and operate a subset of the robot's complement of actuators. The robot may comprise an adaptive controller comprising a neuron network. The adaptive controller may be configured to generate actuator control commands based on the user input and output of the learning process. Training of the adaptive controller may comprise partial set training. The user may train the adaptive controller to operate first actuator subset. Subsequent to learning to operate the first subset, the adaptive controller may be trained to operate another subset of degrees of freedom based on user input via the control apparatus.

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.

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: The safe operation and navigation of robots is an active research topic for many real-world applications, such as the automation of large industrial equipment. This technological field often requires heavy machines with arbitrary shapes to navigate very close to obstacles, a challenging and largely unsolved problem. To address this issue, a new planning architecture is developed that allows wheeled vehicles to navigate safely and without human supervision in cluttered environments. The inventive methods and systems disclosed herein belong to the Model Predictive Control (MPC) family of local planning algorithms. The technological features disclosed herein works in the space of two-dimensional (2D) occupancy grids and plans in motor command space using a black box forward model for state inference. Compared to the conventional methods and systems, the inventive methods and systems disclosed herein include several properties that make it scalable and applicable to a production environment.

Abstract: Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.

Abstract: Robotic devices may be trained by a user guiding the robot along a target trajectory using a correction signal. A robotic device 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. Training may comprise a plurality of trials. During an initial portion of a trial, the trainer may observe robot's operation and refrain from providing the training input to the robot. Upon observing a discrepancy between the target behavior and the actual behavior during the initial trial portion, the trainer may provide a teaching input (e.g., a correction signal) configured to affect robot's trajectory during subsequent trials. Upon completing a sufficient number of trials, the robot may be capable of navigating the trajectory in absence of the training input.

Abstract: The safe operation and navigation of robots is an active research topic for many real-world applications, such as the automation of large industrial equipment. This technological field often requires heavy machines with arbitrary shapes to navigate very close to obstacles, a challenging and largely unsolved problem. To address this issue, a new planning architecture is developed that allows wheeled vehicles to navigate safely and without human supervision in cluttered environments. The inventive methods and systems disclosed herein belong to the Model Predictive Control (MPC) family of local planning algorithms. The technological features disclosed herein works in the space of two-dimensional (2D) occupancy grids and plans in motor command space using a black box forward model for state inference. Compared to the conventional methods and systems, the inventive methods and systems disclosed herein include several properties that make it scalable and applicable to a production environment.

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 for removing false positives from sensor detection for robotic apparatuses are disclosed. According to exemplary embodiments, robot may use a material filter, digital filter, or a combination of digital and material filters of increasing strength to remove the false positive from sensor detection.

Abstract: Systems and methods for global rerouting of a path of a robot are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may reroute a path based on one or more rerouting zones, wherein the rerouting zone comprises an area undesirable for the robot to navigate. Accordingly, the present disclosure provides systems and methods for a robot to reroute a path based on the rerouting zones.

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 and methods for optimizing robotic route planning are disclosed in relation to autonomous navigation of sharp turns, narrow passageways, and/or a sharp turn into a narrow passageway. Robots navigating a route comprising any of the above run the risk of colliding with environment obstacles when executing these maneuvers. Accordingly, systems and methods for improving robotic route planning are necessary within the art and are disclosed herein.

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: 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: Systems and methods for robotic mapping are disclosed. In some exemplary implementations, a robot can travel in an environment. From travelling in the environment, the robot can create a graph comprising a plurality of nodes, wherein each node corresponds to a scan taken by a sensor of the robot at a location in the environment. In some exemplary implementations, the robot can generate a map of the environment from the graph. In some cases, to facilitate map generation, the robot can constrain the graph to start and end at a substantially similar location. The robot can also perform scan matching on extended scan groups, determined from identifying overlap between scans, to further determine the location of features in a map.