公开(公告)号：US11960994B2

公开(公告)日：2024-04-16

申请号：US17151506

申请日：2021-01-18

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Jonathan D. Brookshire ,  Zachary Seymour ,  Niluthpol C. Mithun ,  Supun Samarasekera ,  Rakesh Kumar ,  Qiao Wang

IPC: G06N3/08 ,  G06N3/084 ,  G06N3/096 ,  G06N3/098 ,  G06N20/00

CPC classification number: G06N3/08 ,  G06N3/084 ,  G06N3/096 ,  G06N3/098 ,  G06N20/00

Abstract: A method, apparatus and system for artificial intelligence-based HDRL planning and control for coordinating a team of platforms includes implementing a global planning layer for determining a collective goal and determining, by applying at least one machine learning process, at least one respective platform goal to be achieved by at least one platform, implementing a platform planning layer for determining, by applying at least one machine learning process, at least one respective action to be performed by the at least one of the platforms to achieve the respective platform goal, and implementing a platform control layer for determining at least one respective function to be performed by the at least one of the platforms. In the method, apparatus and system despite the fact that information is shared between at least two of the layers, the global planning layer, the platform planning layer, and the platform control layer are trained separately.

公开(公告)号：US11361470B2

公开(公告)日：2022-06-14

申请号：US16667047

申请日：2019-10-29

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Zachary Seymour ,  Karan Sikka ,  Supun Samarasekera ,  Rakesh Kumar ,  Niluthpol Mithun

IPC: G01S17/89 ,  G01S7/48 ,  G06F16/583 ,  G06K9/62 ,  G06T7/00 ,  G06T7/73 ,  G06V10/44 ,  G06V10/82 ,  G06V20/00

Abstract: A method, apparatus and system for visual localization includes extracting appearance features of an image, extracting semantic features of the image, fusing the extracted appearance features and semantic features, pooling and projecting the fused features into a semantic embedding space having been trained using fused appearance and semantic features of images having known locations, computing a similarity measure between the projected fused features and embedded, fused appearance and semantic features of images, and predicting a location of the image associated with the projected, fused features. An image can include at least one image from a plurality of modalities such as a Light Detection and Ranging image, a Radio Detection and Ranging image, or a 3D Computer Aided Design modeling image, and an image from a different sensor, such as an RGB image sensor, captured from a same geo-location, which is used to determine the semantic features of the multi-modal image.

公开(公告)号：US20240265266A1

公开(公告)日：2024-08-08

申请号：US18434435

申请日：2024-02-06

Applicant: SRI International

Inventor： Theodore Camus ,  Zachary Seymour ,  Bhoram Lee ,  Andrew C. Silberfarb ,  Supun Samarasekera ,  Jonathan Brookshire

IPC: G06N3/092 ,  G06N3/042

CPC classification number: G06N3/092 ,  G06N3/042

Abstract: In general, techniques are described for coordinating actions of a plurality of agents or subsystems using a machine learning system that implements a Capability Graph Network (CGN). In an example, a method includes generating a control policy model comprising a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, wherein the plurality of nodes represents a plurality of agents or subsystems and the plurality of edges represent information exchange between the plurality of agents or subsystems; and encoding agent behavior control policy within the control policy model for executing to coordinate a plurality of the actions of the plurality of agents or subsystems.

公开(公告)号：US20230394294A1

公开(公告)日：2023-12-07

申请号：US17151506

申请日：2021-01-18

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Jonathan D. Brookshire ,  Zachary Seymour ,  Niluthpol C. Mithun ,  Supun Samarasekera ,  Rakesh Kumar ,  Qiao Wang

IPC: G06N3/092 ,  G06N3/04

CPC classification number: G06N3/092 ,  G06N3/04

Abstract: A method, apparatus and system for artificial intelligence-based HDRL planning and control for coordinating a team of platforms includes implementing a global planning layer for determining a collective goal and determining, by applying at least one machine learning process, at least one respective platform goal to be achieved by at least one platform, implementing a platform planning layer for determining, by applying at least one machine learning process, at least one respective action to be performed by the at least one of the platforms to achieve the respective platform goal, and implementing a platform control layer for determining at least one respective function to be performed by the at least one of the platforms. In the method, apparatus and system despite the fact that information is shared between at least two of the layers, the global planning layer, the platform planning layer, and the platform control layer are trained separately.

公开(公告)号：US20240403649A1

公开(公告)日：2024-12-05

申请号：US18520800

申请日：2023-11-28

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Yi Yao ,  Zachary Seymour ,  Alex Krasner ,  Bradley J. Clymer ,  Michael A. Cogswell ,  Cecile Eliane Jeannine Mackay ,  Alex C. Tozzo ,  Tixiao Shan ,  Philip Miller ,  Chuanyong Gan ,  Glenn A. Murray ,  Richard Louis Ferranti ,  Uma Rajendran ,  Supun Samarasekera ,  Rakesh Kumar ,  James Smith

IPC: G06N3/0895

Abstract: In an example, a system includes processing circuitry in communication with storage media. The processing circuitry is configured to execute a machine learning system including at least a first module, a second module and a third module. The machine learning system is configured to train one or more machine learning models. The first module is configured to generate augmented input data based on the streaming input data. The second module includes a machine learning model configured to perform a specific task based at least in part on the augmented input data. The third module configured to adapt a network architecture of the one or more machine learning models based on changes in the streaming input data.

公开(公告)号：US12062186B2

公开(公告)日：2024-08-13

申请号：US17496403

申请日：2021-10-07

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Junjiao Tian ,  Zachary Seymour ,  Niluthpol C. Mithun ,  Alex Krasner ,  Mikhail Sizintsev ,  Abhinav Rajvanshi ,  Kevin Kaighn ,  Philip Miller ,  Ryan Villamil ,  Supun Samarasekera

IPC: G06T7/174 ,  G06T3/40 ,  G06T7/38 ,  G06T7/50

CPC classification number: G06T7/174 ,  G06T3/40 ,  G06T7/38 ,  G06T7/50 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20112

Abstract: A method, machine readable medium and system for RGBD semantic segmentation of video data includes determining semantic segmentation data and depth segmentation data for less than all classes for images of each frame of a first video, determining semantic segmentation data and depth segmentation data for images of each key frame of a second video including a synchronous combination of respective frames of the RGB video and the depth-aware video in parallel to the determination of the semantic segmentation data and the depth segmentation data for each frame of the first video, temporally and geometrically aligning respective frames of the first video and the second video, and predicting semantic segmentation data and depth segmentation data for images of a subsequent frame of the first video based on the determination of the semantic segmentation data and depth segmentation data for images of a key frame of the second video.

公开(公告)号：US20220092366A1

公开(公告)日：2022-03-24

申请号：US17478177

申请日：2021-09-17

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Junjiao Tian ,  Zachary Seymour ,  Niluthpol C. Mithun

IPC: G06K9/62 ,  G06N20/00

Abstract: Techniques are disclosed for an image understanding system comprising a machine learning system that applies a machine learning model to perform image understanding of each pixel of an image, the pixel labeled with a class, to determine an estimated class to which the pixel belongs. The machine learning system determines, based on the classes with which the pixels are labeled and the estimated classes, a cross entropy loss of each class. The machine learning system determines, based on one or more region metrics, a weight for each class and applies the weight to the cross entropy loss of each class to obtain a weighted cross entropy loss. The machine learning system updates the machine learning model with the weighted cross entropy loss to improve a performance metric of the machine learning model for each class.