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1.发明授权Method and system for dynamic task selection suitable for mapping external inputs and internal goals toward actions that solve problems or elicit rewards 有权用于动态任务选择的方法和系统，适用于将外部输入和内部目标映射到解决问题或引发奖励的动作公开(公告)号：US08762305B1
公开(公告)日：2014-06-24
申请号：US13287953
申请日：2011-11-02
CPC分类号： G06N3/008 , G06N3/08 , G06N99/005 , Y10S706/903
摘要： The present invention relates to a system for mapping external inputs and internal goals toward actions that solve problems or elicit external rewards. The present invention allows an instructor to test and train an agent to perform dynamic task selection (executive control) by using a schema that computes the agent's emotional and motivational states from reward/punishment inputs and sensory inputs (visual, auditory, kinematic, tactile, olfactory, somatosensory, and motor inputs). Specifically, the invention transforms the sensory inputs into unimodal and bimodal spatio-temporal schemas that are combined with the reward/punishment inputs and with the emotional and motivation states to create an external/internal schema (EXIN schema), that provides a compressed representation assessing the agent's emotions, motivations, and rewards. The invention uses the EXIN schema to create a motor schema to be executed by the agent to dynamically perform the task selected by the instructor.
摘要翻译： 本发明涉及一种用于将外部输入和内部目标映射到解决问题或引发外部奖励的动作的系统。 本发明允许教师通过使用从奖励/惩罚输入和感觉输入（视觉，听觉，运动学，触觉学习和计算机学习）来计算代理人的情绪和动机状态的模式来测试和训练代理来执行动态任务选择（执行控制） 嗅觉，体感和运动输入）。 具体来说，本发明将感官输入转换成单峰和双峰时空模式，其与奖励/惩罚输入以及情绪和动机状态结合以创建外部/内部模式（EXIN模式），其提供压缩表示评估 代理人的情绪，动机和奖励。 本发明使用EXIN模式来创建要由代理执行的运动模式以动态地执行教师所选择的任务。
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公开(公告)号：US08468104B1
公开(公告)日：2013-06-18
申请号：US12592837
申请日：2009-12-02
IPC分类号： G06F15/18
CPC分类号： G06K9/6222 , G01S13/887
摘要： Described is a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing. The system is configured to receive, in a processor, at least one streaming peaked curve (R) representative of a difference between an input and a chosen category for a given feature. A degree of match is then generated between the input and the chosen category for all features. Finally, the degree of match is compared against a predetermined anomaly threshold and, if the degree of match exceeds the predetermined anomaly threshold, then the current feature is designated as an anomaly.
摘要翻译： 描述了用于异常检测的系统，用于检测图像中的异常物体，例如人的衣服下方的隐藏物体。 系统被配置为在处理器中接收代表给定特征的输入和所选类别之间的差异的至少一个流式峰值曲线（R）。 然后在所有功能的输入和所选类别之间生成匹配度。 最后，将匹配度与预定的异常阈值进行比较，并且如果匹配度超过预定的异常阈值，则将当前特征指定为异常。
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公开(公告)号：US20110071675A1
公开(公告)日：2011-03-24
申请号：US12564074
申请日：2009-09-22
申请人： James W. Wells , Neil David Mc Kay , Suhas E. Chelian , Douglas Martin Linn , Charles W. Wampler, II , Lyndon Bridgwater
发明人： James W. Wells , Neil David Mc Kay , Suhas E. Chelian , Douglas Martin Linn , Charles W. Wampler, II , Lyndon Bridgwater
CPC分类号： G06K9/3216 , G06T7/246 , G06T2207/10016 , G06T2207/10028 , G06T2207/10048 , G06T2207/20081 , H04N5/247 , H04N5/332 , H04N13/204
摘要： A robotic system includes a humanoid robot with robotic joints each moveable using an actuator(s), and a distributed controller for controlling the movement of each of the robotic joints. The controller includes a visual perception module (VPM) for visually identifying and tracking an object in the field of view of the robot under threshold lighting conditions. The VPM includes optical devices for collecting an image of the object, a positional extraction device, and a host machine having an algorithm for processing the image and positional information. The algorithm visually identifies and tracks the object, and automatically adapts an exposure time of the optical devices to prevent feature data loss of the image under the threshold lighting conditions. A method of identifying and tracking the object includes collecting the image, extracting positional information of the object, and automatically adapting the exposure time to thereby prevent feature data loss of the image.
摘要翻译： 机器人系统包括具有机器人接头的人形机器人，每个机器人接头均可使用致动器可移动;以及分布式控制器，用于控制每个机器人接头的运动。 控制器包括视觉识别模块（VPM），用于在阈值照明条件下可视地识别和跟踪机器人视野中的对象。 VPM包括用于收集对象的图像的光学装置，位置提取装置和具有用于处理图像和位置信息的算法的主机。 该算法可视地识别和跟踪对象，并自动调整光学设备的曝光时间，以防止阈值照明条件下图像的特征数据丢失。 识别和跟踪对象的方法包括收集图像，提取对象的位置信息，并自动调整曝光时间，从而防止图像的特征数据丢失。
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公开(公告)号：US09129158B1
公开(公告)日：2015-09-08
申请号：US13412527
申请日：2012-03-05
申请人： Swarup Medasani , Suhas E. Chelian , Shinko Y. Cheng , Rashmi N. Sundareswara , Howard Neely, III
发明人： Swarup Medasani , Suhas E. Chelian , Shinko Y. Cheng , Rashmi N. Sundareswara , Howard Neely, III
CPC分类号： G06K9/00718 , G06K9/00335 , G06K9/00342 , G06K9/6219 , G06K9/6263 , G06K9/6296 , G06T2207/10016
摘要： Described is a method and system for embedding unsupervised learning into three critical processing stages of the spatio-temporal visual stream. The system first receives input video comprising input video pixels representing at least one action and at least one object having a location. Microactions are generated from the input image using a set of motion sensitive filters. A relationship between the input video pixels and the microactions is then learned, and a set of spatio-temporal concepts is learned from the microactions. The system then learns to acquire new knowledge from the spatio-temporal concepts using mental imagery processes. Finally, a visual output is presented to a user based on the learned set of spatio-temporal concepts and the new knowledge to aid the user in visually comprehending the at least one action in the input video.
摘要翻译： 描述了将无监督学习嵌入时空视觉流的三个关键处理阶段的方法和系统。 系统首先接收包括表示至少一个动作的输入视频像素和至少一个具有位置的对象的输入视频。 使用一组运动敏感滤波器从输入图像生成微反应。 然后学习输入视频像素和微动作之间的关系，并从微动态学习一组时空概念。 该系统然后学习从使用心理图像过程的时空概念中获取新知识。 最后，基于学习的时空概念和新知识，向用户呈现视觉输出，以帮助用户直观地理解输入视频中的至少一个动作。
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公开(公告)号：US08948499B1
公开(公告)日：2015-02-03
申请号：US12962548
申请日：2010-12-07
IPC分类号： G06K9/62
CPC分类号： G06K9/469 , G06K9/00771 , G06K9/00785 , G06K9/6296
摘要： Described is a system for object and behavior recognition which utilizes a collection of modules which, when integrated, can automatically recognize, learn, and adapt to simple and complex visual behaviors. An object recognition module utilizes a cooperative swarm algorithm to classify an object in a domain. A graph-based object representation module is configured to use a graphical model to represent a spatial organization of the object within the domain. Additionally, a reasoning and recognition engine module consists of two sub-modules: a knowledge sub-module and a behavior recognition sub-module. The knowledge sub-module utilizes a Bayesian network, while the behavior recognition sub-module consists of layers of adaptive resonance theory clustering networks and a layer of a sustained temporal order recurrent temporal order network. The described invention has applications in video forensics, data mining, and intelligent video archiving.
摘要翻译： 描述了一种用于对象和行为识别的系统，其利用模块集合，当集成时，可以自动识别，学习和适应简单和复杂的视觉行为。 对象识别模块利用协作群算法对域中的对象进行分类。 基于图形的对象表示模块被配置为使用图形模型来表示域内对象的空间组织。 另外，推理和识别引擎模块由两个子模块组成：知识子模块和行为识别子模块。 知识子模块利用贝叶斯网络，而行为识别子模块由自适应共振理论聚类网络层和持续时间顺序复现时间顺序网络层组成。 所描述的发明在视频取证，数据挖掘和智能视频归档中具有应用。
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公开(公告)号：US08515160B1
公开(公告)日：2013-08-20
申请号：US12316941
申请日：2008-12-17
申请人： Deepak Khosla , Suhas E. Chelian
发明人： Deepak Khosla , Suhas E. Chelian
CPC分类号： G06K9/4623 , G06K9/3241
摘要： A bio-inspired actionable intelligence method and system is disclosed. The actionable intelligence method comprises recognizing entities in an imagery signal, detecting and classifying anomalous entities, and learning new hierarchal relationships between different classes of entities. A knowledge database is updated after each new learning experience to aid in future searches and classification. The method can accommodate incremental learning via Adaptive Resonance Theory (ART).
摘要翻译： 公开了一种生物灵感的可操作智能方法和系统。 可操作的智能方法包括识别图像信号中的实体，检测和分类异常实体，以及学习不同类别的实体之间的新的层次关系。 每个新的学习经验之后更新知识数据库，以帮助未来的搜索和分类。 该方法可以通过自适应共振理论（ART）来适应增量学习。
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公开(公告)号：US08244402B2
公开(公告)日：2012-08-14
申请号：US12564074
申请日：2009-09-22
申请人： James W. Wells , Neil David Mc Kay , Suhas E. Chelian , Douglas Martin Linn , Charles W. Wampler, II , Lyndon Bridgwater
发明人： James W. Wells , Neil David Mc Kay , Suhas E. Chelian , Douglas Martin Linn , Charles W. Wampler, II , Lyndon Bridgwater
IPC分类号： G05B19/04
CPC分类号： G06K9/3216 , G06T7/246 , G06T2207/10016 , G06T2207/10028 , G06T2207/10048 , G06T2207/20081 , H04N5/247 , H04N5/332 , H04N13/204
摘要： A robotic system includes a humanoid robot with robotic joints each moveable using an actuator(s), and a distributed controller for controlling the movement of each of the robotic joints. The controller includes a visual perception module (VPM) for visually identifying and tracking an object in the field of view of the robot under threshold lighting conditions. The VPM includes optical devices for collecting an image of the object, a positional extraction device, and a host machine having an algorithm for processing the image and positional information. The algorithm visually identifies and tracks the object, and automatically adapts an exposure time of the optical devices to prevent feature data loss of the image under the threshold lighting conditions. A method of identifying and tracking the object includes collecting the image, extracting positional information of the object, and automatically adapting the exposure time to thereby prevent feature data loss of the image.
摘要翻译： 机器人系统包括具有机器人接头的人形机器人，每个机器人接头均可使用致动器可移动;以及分布式控制器，用于控制每个机器人接头的运动。 控制器包括视觉识别模块（VPM），用于在阈值照明条件下可视地识别和跟踪机器人视野中的对象。 VPM包括用于收集对象的图像的光学装置，位置提取装置和具有用于处理图像和位置信息的算法的主机。 该算法可视地识别和跟踪对象，并自动调整光学设备的曝光时间，以防止阈值照明条件下图像的特征数据丢失。 识别和跟踪对象的方法包括收集图像，提取对象的位置信息，并自动调整曝光时间，从而防止图像的特征数据丢失。
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