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公开(公告)号：US11161241B2
公开(公告)日：2021-11-02
申请号：US16269129
申请日：2019-02-06
Applicant: Brain Corporation
Inventor： Oleg Sinyavskiy , Jean-Baptiste Passot , Eugene Izhikevich
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.
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公开(公告)号：US20210223779A1
公开(公告)日：2021-07-22
申请号：US17205692
申请日：2021-03-18
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot
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.
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公开(公告)号：US10899008B2
公开(公告)日：2021-01-26
申请号：US16376237
申请日：2019-04-05
Applicant: BRAIN CORPORATION
Inventor： Oleg Sinyavskiy , Jean-Baptiste Passot , Borja Ibarz Gabardos , Diana Vu Le
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.
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公开(公告)号：US20200316773A1
公开(公告)日：2020-10-08
申请号：US16908038
申请日：2020-06-22
Applicant: Brain Corporation
Inventor： Eugene Izhikevich , Oleg Sinyavskiy , Jean-Baptiste Passot
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.
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公开(公告)号：US20200004253A1
公开(公告)日：2020-01-02
申请号：US16454217
申请日：2019-06-27
Applicant: Brain Corporation
Inventor： Borja Ibarz Gabardos , Jean-Baptiste Passot
Abstract: Systems and methods for dynamic route planning in autonomous navigation are disclosed. In some exemplary implementations, a robot can have one or more sensors configured to collect data about an environment including detected points on one or more objects in the environment. The robot can then plan a route in the environment, where the route can comprise one or more route poses. The route poses can include a footprint indicative at least in part of a pose, size, and shape of the robot along the route. Each route pose can have a plurality of points therein. Based on forces exerted on the points of each route pose by other route poses, objects in the environment, and others, each route poses can reposition. Based at least in part on interpolation performed on the route poses (some of which may be repositioned), the robot can dynamically route.
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Patent Agency Ranking
公开(公告)号：US10507580B2
公开(公告)日：2019-12-17
申请号：US15967240
申请日：2018-04-30
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Oleg Sinyavskiy , Eugene Izhikevich
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.
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公开(公告)号：US20190321973A1
公开(公告)日：2019-10-24
申请号：US16402758
申请日：2019-05-03
Applicant: Brain Corporation
Inventor： Philip Meier , Jean-Baptiste Passot , Borja Ibarz Gabardos , Patryk Laurent , Oleg Sinyavskiy , Peter O'Connor , Eugene Izhikevich
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.
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公开(公告)号：US20190249998A1
公开(公告)日：2019-08-15
申请号：US16356160
申请日：2019-03-18
Applicant: BRAIN CORPORATION
Inventor： Jaldert Rombouts , Borja Ibarz Gabardos , Jean-Baptiste Passot , Andrew Smith
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.
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公开(公告)号：US10369694B2
公开(公告)日：2019-08-06
申请号：US15960300
申请日：2018-04-23
Applicant: Brain Corporation
Inventor： Patryk Laurent , Jean-Baptiste Passot , Oleg Sinyavskiy , Filip Ponulak , Borja Ibarz Gabardos , Eugene Izhikevich
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.
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公开(公告)号：US20190235512A1
公开(公告)日：2019-08-01
申请号：US16260590
申请日：2019-01-29
Applicant: Brain Corporation
Inventor： Oleg Sinyavskiy , Borja Ibarz Gabardos , Jean-Baptiste Passot
IPC: G05D1/02
CPC classification number: G05D1/0217 , G05D1/0238 , G05D1/0274
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. The inventive concepts disclosed herein are at least deterministic, computationally efficient, run in constant time and can be deployed in many common non-holonomic systems.
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