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公开(公告)号：US20160279790A1
公开(公告)日：2016-09-29
申请号：US15174858
申请日：2016-06-06
Applicant: Brain Corporation
Inventor： Philip Meier , Jean-Baptiste Passot , Borja Ibarz Gabardos , Patryk Laurent , Oleg Sinyavskiy , Peter O'Connor , Eugene Izhikevich
CPC classification number: B25J9/163 , B25J9/1602 , B25J9/161 , B25J9/1656 , G05B2219/40116 , G05D1/0033 , G05D1/0088 , G05D2201/02 , G06N3/008 , G06N99/005 , Y10S901/01 , Y10S901/46
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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42.发明申请公开(公告)号：US20160096270A1
公开(公告)日：2016-04-07
申请号：US14542391
申请日：2014-11-14
Applicant: Brain Corporation
Inventor： Borja Ibarz Gabardos , Andrew Smith , Peter O'Connor
IPC: B25J9/16
CPC classification number: B25J9/163 , B25J9/1607 , B25J9/1666 , B25J9/1697 , G05D1/0088 , G05D1/0246 , G05D2201/02 , G06N3/00 , G06N3/008 , G06N3/049 , Y10S901/01 , Y10S901/03 , Y10S901/09 , Y10S901/47
Abstract: A robotic device may be operated by a learning controller comprising a feature learning configured to determine control signal based on sensory input. An input may be analyzed in order to determine occurrence of one or more features. Features in the input may be associated with the control signal during online supervised training. During training, learning process may be adapted based on training input and the predicted output. A combination of the predicted and the target output may be provided to a robotic device to execute a task. Feature determination may comprise online adaptation of input, sparse encoding transformations. Computations related to learning process adaptation and feature detection may be performed on board by the robotic device in real time thereby enabling autonomous navigation by trained robots.
Abstract translation: 机器人设备可以由包括被配置为基于感觉输入来确定控制信号的特征学习的学习控制器操作。 可以分析输入以确定一个或多个特征的发生。 输入中的特征可能与在线监督训练期间的控制信号相关联。 在训练中，学习过程可以根据训练输入和预测输出进行调整。 可以将预测和目标输出的组合提供给机器人装置以执行任务。 特征确定可以包括输入，稀疏编码变换的在线适配。 与学习过程适应和特征检测相关的计算可以由机器人设备实时执行，从而使训练有素的机器人能够进行自主导航。
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公开(公告)号：US20140371907A1
公开(公告)日：2014-12-18
申请号：US13918338
申请日：2013-06-14
Applicant: BRAIN CORPORATION
Inventor： Jean-Baptiste Passot , Oleg Sinyavskiy , Filip Ponulak , Patryk Laurent , Borja Ibarz Gabardos , Eugene Izhikevich
IPC: B25J9/16
Abstract: Apparatus and methods for training of robotic devices. Robotic devices may be trained by a user guiding the robot along target trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control commands based on one or more of the user guidance, sensory input, and/or performance measure. Training may comprise a plurality of trials. During first trial, the user input may be sufficient to cause the robot to complete the trajectory. During subsequent trials, the user and the robot's controller may collaborate so that user input may be reduced while the robot control may be increased. Individual contributions from the user and the robot controller during training may be may be inadequate (when used exclusively) to complete the task. Upon learning, user's knowledge may be transferred to the robot's controller to enable task execution in absence of subsequent inputs from the user
Abstract translation: 用于训练机器人装置的装置和方法。 机器人装置可以由用户使用输入信号沿目标轨迹引导机器人进行训练。 机器人设备可以包括自适应控制器，其被配置为基于用户引导，感觉输入和/或性能测量中的一个或多个来产生控制命令。 培训可能包括多项试验。 在第一次试用期间，用户输入可能足以使机器人完成轨迹。 在随后的试验期间，用户和机器人的控制器可以协作，以便可以减少用户输入，同时可以增加机器人控制。 在训练期间来自用户和机器人控制器的个人贡献可能不足以（完全用于完成任务）。 在学习之后，用户的知识可以传送到机器人的控制器，以便在没有用户的后续输入的情况下执行任务执行
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公开(公告)号：US11467602B2
公开(公告)日：2022-10-11
申请号：US16168368
申请日：2018-10-23
Applicant: Brain Corporation
Inventor： Jean-Baptiste Passot , Andrew Smith , Botond Szatmary , Borja Ibarz Gabardos , Cody Griffin , Jaldert Rombouts , Oleg Sinyavskiy , Eugene Izhikevich
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.
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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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Patent Agency Ranking
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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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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