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1. 发明授权

US11625557B2 Process to learn new image classes without labels 有权
公开(公告)号：US11625557B2
公开(公告)日：2023-04-11
申请号：US17080673
申请日：2020-10-26
申请人： HRL Laboratories, LLC
发明人： Heiko Hoffmann , Soheil Kolouri
IPC分类号： G06K9/62 , B60W50/06 , G06N20/00 , G06N3/02 , B60W60/00
摘要： Described is a system for learning object labels for control of an autonomous platform. Pseudo-task optimization is performed to identify an optimal pseudo-task for each source model of one or more source models. An initial target network is trained using the optimal pseudo-task. Source image components are extracted from source models, and an attribute dictionary of attributes is generated from the source image components. Using zero-shot attribution distillation, the unlabeled target data is aligned with the source models similar to the unlabeled target data. The unlabeled target data are mapped onto attributes in the attribute dictionary. A new target network is generated from the mapping, and the new target network is used to assign an object label to an object in the unlabeled target data. The autonomous platform is controlled based on the object label.

2. 发明授权

US10926776B1 Process to control a networked system of time varying devices 有权
公开(公告)号：US10926776B1
公开(公告)日：2021-02-23
申请号：US16241854
申请日：2019-01-07
申请人： HRL Laboratories, LLC
发明人： Byron N. Heersink , Heiko Hoffmann , Michael A. Warren
IPC分类号： B60W50/00 , G06F16/901 , G05D1/00 , B60R16/023
摘要： Described is a control command system for generating control commands for a vehicle. The system receives a networked control system corresponding to a network of subsystems of sensors and actuators of the vehicle and data collected from sensors on the vehicle. Multiple subsystems are formed from the networked control system. A dynamic mode decomposition with control (DMDc) process is applied to each subsystem, yielding a linear approximation for each subsystem. The linear approximations are combined into a single linear approximation for the networked control system, and a linear control system approximating the networked control system is output. Control commands are generated for the vehicle based on the linear control system, which cause the vehicle to perform a vehicle operation.

3. 发明授权

US10607111B2 Machine vision system for recognizing novel objects 有权
公开(公告)号：US10607111B2
公开(公告)日：2020-03-31
申请号：US16267033
申请日：2019-02-04
申请人： HRL Laboratories, LLC
发明人： Soheil Kolouri , Charles E. Martin , Kyungnam Kim , Heiko Hoffmann
IPC分类号： G06K9/00 , G06K9/62 , G06K9/46 , G06T7/40 , G06T1/00 , G06N3/04
摘要： Described is a system for classifying novel objects in imagery. In operation, the system extracts salient patches from a plurality of unannotated images using a multi-layer network. Activations of the multi-layer network are clustered into key attribute, with the key attributes being displayed to a user on a display, thereby prompting the user to annotate the key attributes with class label. An attribute database is then generated based on user prompted annotations of the key attributes. A test image can then be passed through the system, allowing the system to classify at least one object in the test image by identifying an object class in the attribute database. Finally, a device can be caused to operate or maneuver based on the classification of the at least one object in the test image.

4. 发明授权

US10580142B1 System and method for self-organized critical image segmentation 有权
公开(公告)号：US10580142B1
公开(公告)日：2020-03-03
申请号：US15480319
申请日：2017-04-05
申请人： HRL Laboratories, LLC
发明人： Heiko Hoffmann , David W. Payton
IPC分类号： G06T7/11 , G06T7/194
摘要： Described is a system for self-organized critical image segmentation. During operation, the system generates a delta pattern from a self-organized critical process. An initial test pattern is then altered based on the delta pattern to generate a new test pattern. The new test pattern is a mask identifying distinct regions in an image. A new energy score is then generated of the new test pattern. The operations of generating the delta pattern and altering the initial test pattern are then repeated until an energy score of the new test pattern is less than an energy score of the initial test pattern. At that point, the initial test pattern is replaced with the new test pattern. Finally, the process is repeated until a termination condition is reached, at which point the new test pattern provides the image segmentation by dividing the image into distinct regions, including a foreground and background.

5. 发明授权

US10095976B1 Neural network device with engineered delays for pattern storage and matching 有权
公开(公告)号：US10095976B1
公开(公告)日：2018-10-09
申请号：US15057102
申请日：2016-02-29
申请人： HRL Laboratories, LLC
发明人： Heiko Hoffmann
IPC分类号： G06F15/18 , G06N3/04 , G06N3/08 , G06F17/30
摘要： Described is a system for searching a continuous data stream for exact matches with a priori stored data sequences. The system includes a neural network with an input and an output layer. The input layer has one neuron for each possible character or number in the data stream, and the output layer has one neuron for each stored pattern. Importantly, the delays of the connections from input to output layer are engineered to match the temporal occurrence of an input character within a stored sequence. Thus, if an input sequence has the proper time gaps between characters, matching a stored pattern, then the delayed neural signals result in a simultaneous activation at the receiving neuron, which indicates a detected pattern. For storing a pattern, only one connection for each pair of input character and output neuron has to be specified resulting in sparse coding and quick storage.

6. 发明授权

US10052062B2 System and method for assistive gait intervention and fall prevention 有权
公开(公告)号：US10052062B2
公开(公告)日：2018-08-21
申请号：US15042078
申请日：2016-02-11
申请人： HRL Laboratories, LLC
发明人： Vincent De Sapio , Michael D. Howard , Suhas E. Chelian , Matthias Ziegler , Matthew E. Phillips , Kevin R. Martin , Heiko Hoffmann , David W. Payton
IPC分类号： A61B5/00 , A61B5/11 , A61B5/0488 , A61N1/36
CPC分类号： A61B5/4836 , A61B5/0488 , A61B5/1117 , A61B5/112 , A61B5/1122 , A61B5/1123 , A61B5/4023 , A61B5/6804 , A61B5/7275 , A61B5/7455 , A61B5/746 , A61B2562/0219 , A61N1/0484 , A61N1/36003 , A61N1/36031 , A61N1/36036 , G16H20/30 , G16H40/63 , G16H50/30
摘要： Described is a system for system for gait intervention and fall prevention. The system is incorporated into a body suit having a plurality of distributed sensors and a vestibulo-muscular biostim array. The sensors are operable for providing biosensor data to the analytics module, while the vestibulo-muscular biostim array includes a plurality of distributed effectors. The analytics module is connected with the body suit and sensors and is operable for receiving biosensor data and analyzing a particular user's gait and predicting falls. Finally, a closed-loop biostim control module is included for activating the vestibulo-muscular biostim array to compensate for a risk of a predicted fall.

7. 发明申请

US20170318032A1 SYSTEM AND METHOD FOR DETECTING ATTACKS ON MOBILE AD HOC NETWORKS BASED ON NETWORK FLUX 审中-公开
公开(公告)号：US20170318032A1
公开(公告)日：2017-11-02
申请号：US15073158
申请日：2016-03-17
申请人： HRL Laboratories, LLC
发明人： Heiko Hoffmann , Gavin D. Holland
IPC分类号： H04L29/06
CPC分类号： H04L63/1416 , H04L12/22 , H04L29/06 , H04L63/1408 , H04L63/1425 , H04L63/1433 , H04L63/1441 , H04L63/1458 , H04L63/162 , H04L2463/143 , H04W12/12 , H04W84/18
摘要： Described is a system for detecting attacks on mobile networks. The system includes the relevant hardware and components to perform a variety of operations including continuously measuring time-varying signals at each node in a network. The system determines network flux on the time-varying signals of all nodes in the network and detects a network attack if the network flux exceeds a predetermined threshold. Further, a reactive protocol is initiated if the network flux exceeds the predetermined threshold.

8. 发明授权

US09364951B1 System for controlling motion and constraint forces in a robotic system 有权
标题翻译：用于控制机器人系统中运动和约束力的系统
公开(公告)号：US09364951B1
公开(公告)日：2016-06-14
申请号：US14513583
申请日：2014-10-14
申请人： HRL Laboratories, LLC
发明人： Vincent De Sapio , Heiko Hoffmann
IPC分类号： B25J9/16 , G05B19/19 , B25J9/00
CPC分类号： B25J9/16 , B25J9/0006 , B25J9/0084 , B25J9/1633 , G05B19/19 , G05B2219/34351 , G05B2219/36432 , G05B2219/40359
摘要： Described is a system for controlling motion and constraint forces in a robotic system, the system infers constraints, at each time step, from a sensed state of robot/environment interactions. The inferred constraints are appended to known internal robot constraints to generate constrained dynamics. Properties associated with the constrained dynamics are determined provided to a controller. Inequality conditions associated with maintaining desired robot/environment interactions are also determined. A set of equality conditions based on the inequality conditions are then specified. The set of equality conditions are aggregated with any internal robot constraints to generate aggregated conditions that are provided to the controller. Joint torque commands are then generated for the robot based on the aggregated conditions and a specified task and null space motion command. Finally, the robot is actuated based on the joint torque commands.
摘要翻译：描述了一种用于控制机器人系统中的运动和约束力的系统，该系统在每个时间步骤中从感测到的机器人/环境相互作用状态推断出约束。推断的约束被附加到已知的内部机器人约束以产生约束的动力学。确定与受限动态相关联的属性提供给控制器。还确定了与保持期望的机器人/环境相互作用相关联的不平等条件。然后指定一组基于不等式条件的等式条件。该等式条件的集合与任何内部机器人约束进行聚合，以生成提供给控制器的汇总条件。然后根据聚合条件和指定的任务和零空间运动命令为机器人生成关节转矩指令。最后，机器人基于关节扭矩命令被致动。

9. 发明授权

US09002098B1 Robotic visual perception system 有权
标题翻译：机器人视觉感知系统
公开(公告)号：US09002098B1
公开(公告)日：2015-04-07
申请号：US13721007
申请日：2012-12-19
申请人： HRL Laboratories, LLC
发明人： Suhas E. Chelian , Rashmi N. Sundareswara , Heiko Hoffmann
IPC分类号： G06K9/62 , G06T1/00
CPC分类号： G06K9/50 , G06K9/4642 , G06K9/468 , G06T7/74 , G06T2207/30164 , Y10S901/47
摘要： Described is a robotic visual perception system for determining a position and pose of a three-dimensional object. The system receives an external input to select an object of interest. The system also receives visual input from a sensor of a robotic controller that senses the object of interest. Rotation-invariant shape features and appearance are extracted from the sensed object of interest and a set of object templates. A match is identified between the sensed object of interest and an object template using shape features. The match between the sensed object of interest and the object template is confirmed using appearance features. The sensed object is then identified, and a three-dimensional pose of the sensed object of interest is determined. Based on the determined three-dimensional pose of the sensed object, the robotic controller is used to grasp and manipulate the sensed object of interest.
摘要翻译：描述了一种用于确定三维物体的位置和姿态的机器人视觉感知系统。系统接收外部输入以选择感兴趣的对象。该系统还从感测感兴趣对象的机器人控制器的传感器接收视觉输入。从感兴趣的对象和一组对象模板中提取旋转不变的形状特征和外观。在所感测的感兴趣对象和使用形状特征的对象模板之间识别匹配。使用外观特征确认所感测的感兴趣对象与对象模板之间的匹配。然后识别所感测到的物体，并且确定所感测的感兴趣物体的三维姿态。基于所确定的感测对象的三维姿态，机器人控制器用于掌握和操纵感测到的感兴趣物体。

10. 发明授权

US11288498B2 Learning actions with few labels in the embedded space 有权
公开(公告)号：US11288498B2
公开(公告)日：2022-03-29
申请号：US16931420
申请日：2020-07-16
申请人： HRL Laboratories, LLC
发明人： Amir M. Rahimi , Hyukseong Kwon , Heiko Hoffmann , Soheil Kolouri
IPC分类号： G06K9/00 , G06K9/62 , G06T7/70
摘要： Described is a system for learning actions for image-based action recognition in an autonomous vehicle. The system separates a set of labeled action image data from a source domain into components. The components are mapped onto a set of action patterns, thereby creating a dictionary of action patterns. For each action in the set of labeled action data, a mapping is learned from the action pattern representing the action onto a class label for the action. The system then maps a set of new unlabeled target action image data onto a shared embedding feature space in which action patterns can be discriminated. For each target action in the set of new unlabeled target action image data, a class label for the target action is identified. Based on the identified class label, the autonomous vehicle is caused to perform a vehicle maneuver corresponding to the identified class label.

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