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：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：US20240312197A1

公开(公告)日：2024-09-19

申请号：US18605594

申请日：2024-03-14

Applicant: SRI International

Inventor： Han-Pang Chiu ,  Niluthpol C. Mithun ,  Supun Samarasekera ,  Abhinav Rajvanshi ,  Xingchen Zhao ,  Md Nazmul Karim

IPC: G06V10/82 ,  G06V10/771 ,  G06V10/774 ,  G06V10/776

CPC classification number: G06V10/82 ,  G06V10/771 ,  G06V10/7753 ,  G06V10/776

Abstract: In general, techniques are described for unsupervised domain adaptation of models with pseudo-label curation. In an example, a method includes generating a plurality of pseudo-labels for a dataset of unlabeled data using a source machine learning model; estimating a reliability of each pseudo-label of the plurality of pseudo-labels using one or more reliability measures; selecting a subset of the plurality of pseudo-labels having estimated reliabilities that satisfy a reliability threshold; and training, using one or more curriculum learning techniques, a target machine learning model starting with the selected subset of the plurality of pseudo-labels and the corresponding unlabeled 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.