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公开(公告)号：US20230294277A1
公开(公告)日：2023-09-21
申请号：US17854730
申请日：2022-06-30
Applicant: Nvidia Corporation
Inventor： Wei Yang , Balakumar Sundaralingam , Christopher Jason Paxton , Maya Cakmak , Yu-Wei Chao , Dieter Fox , Iretiayo Akinola
IPC: B25J9/16 , G05B19/4155
CPC classification number: B25J9/1612 , G05B19/4155 , B25J9/1666 , B25J9/1605 , G05B2219/50391 , G05B2219/40269
Abstract: Approaches presented herein provide for predictive control of a robot or automated assembly in performing a specific task. A task to be performed may depend on the location and orientation of the robot performing that task. A predictive control system can determine a state of a physical environment at each of a series of time steps, and can select an appropriate location and orientation at each of those time steps. At individual time steps, an optimization process can determine a sequence of future motions or accelerations to be taken that comply with one or more constraints on that motion. For example, at individual time steps, a respective action in the sequence may be performed, then another motion sequence predicted for a next time step, which can help drive robot motion based upon predicted future motion and allow for quick reactions.
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公开(公告)号：US20230294276A1
公开(公告)日：2023-09-21
申请号：US18148548
申请日：2022-12-30
Applicant: Nvidia Corporation
Inventor： Yu-Wei Chao , Yu Xiang , Wei Yang , Dieter Fox , Chris Paxton , Balakumar Sundaralingam , Maya Cakmak
IPC: B25J9/16
CPC classification number: B25J9/1605 , B25J9/163 , G05B2219/39001
Abstract: Approaches presented herein provide for simulation of human motion for human-robot interactions, such as may involve a handover of an object. Motion capture can be performed for a hand grasping and moving an object to a location and orientation appropriate for a handover, without a need for a robot to be present or an actual handover to occur. This motion data can be used to separately model the hand and the object for use in a handover simulation, where a component such as a physics engine may be used to ensure realistic modeling of the motion or behavior. During a simulation, a robot control model or algorithm can predict an optimal location and orientation to grasp an object, and an optimal path to move to that location and orientation, using a control model or algorithm trained, based at least in part, using the motion models for the hand and object.
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公开(公告)号：US20230202031A1
公开(公告)日：2023-06-29
申请号：US18116118
申请日：2023-03-01
Applicant: NVIDIA Corporation
Inventor： Wei Yang , Christopher Jason Paxton , Yu-Wei Chao , Dieter Fox
CPC classification number: B25J9/1612 , G06T7/50 , G06V20/30 , G06V20/64 , G06V40/107 , G06T2207/10028 , B25J9/1697 , B25J9/16
Abstract: A robotic control system directs a robot to take an object from a human grasp by obtaining an image of a human hand holding an object, estimating the pose of the human hand and the object, and determining a grasp pose for the robot that will not interfere with the human hand. In at least one example, a depth camera is used to obtain a point cloud of the human hand holding the object. The point cloud is provided to a deep network that is trained to generate a grasp pose for a robotic gripper that can take the object from the human's hand without pinching or touching the human's fingers.
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公开(公告)号：US20240261971A1
公开(公告)日：2024-08-08
申请号：US18232217
申请日：2023-08-09
Applicant: NVIDIA Corporation
Inventor： Yuzhe Qin , Wei Yang , Yu-Wei Chao , Dieter Fox
CPC classification number: B25J9/1689 , B25J9/1697 , B25J19/023 , G06T7/50 , G06T7/70 , G06V10/82 , G06V40/10 , G06T2207/10028 , G06T2207/20084
Abstract: Apparatuses, systems, and techniques to generate control commands. In at least one embodiment, control commands are generated based on, for example, one or more images depicting a hand.
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Patent Agency Ranking
公开(公告)号：US20230145208A1
公开(公告)日：2023-05-11
申请号：US17982401
申请日：2022-11-07
Applicant: NVIDIA Corporation
Inventor： Andreea Bobu , Balakumar Sundaralingam , Christopher Jason Paxton , Maya Cakmak , Wei Yang , Yu-Wei Chao , Dieter Fox
Abstract: Apparatuses, systems, and techniques to train a machine learning model. In at least one embodiment, a first machine learning model is trained to infer a concept based on first information, training data is labeled using the first machine learning model, and a second machine learning model is trained to infer the concept using the labeled training data.
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公开(公告)号：US20210086364A1
公开(公告)日：2021-03-25
申请号：US16932067
申请日：2020-07-17
Applicant: NVIDIA Corporation
Inventor： Ankur Handa , Karl Van Wyk , Wei Yang , Yu-Wei Chao , Dieter Fox , Qian Wan
Abstract: A human pilot controls a robotic arm and gripper by simulating a set of desired motions with the human hand. In at least one embodiment, one or more images of the pilot's hand are captured and analyzed to determine a set of hand poses. In at least one embodiment, the set of hand poses is translated to a corresponding set of robotic-gripper poses. In at least one embodiment, a set of motions is determined that perform the set of robotic-gripper poses, and the robot is directed to perform the set of motions.
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公开(公告)号：US20240157557A1
公开(公告)日：2024-05-16
申请号：US18125503
申请日：2023-03-23
Applicant: NVIDIA Corporation
Inventor： Sammy Joe Christen , Wei Yang , Claudia Perez D'Arpino , Dieter Fox , Yu-Wei Chao
IPC: B25J9/16 , G05B19/4155 , G06N3/08
CPC classification number: B25J9/1666 , B25J9/161 , B25J9/1612 , B25J9/163 , B25J9/1697 , G05B19/4155 , G06N3/08 , G05B2219/40202
Abstract: Apparatuses, systems, and techniques to control a real-world and/or virtual device (e.g., a robot). In at least one embodiment, the device is controlled based, at least in part on, for example, one or more neural networks. Parameter values for the neural network(s) may be obtained by training the neural network(s) to control movement of a first agent with respect to at least one first target while avoiding collision with at least one stationary first holder of the at least one first target, and updating the parameter values by training the neural network(s) to control movement of a second agent with respect to at least one second target while avoiding collision with at least one non-stationary second holder of the at least one second target.
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公开(公告)号：US11597078B2
公开(公告)日：2023-03-07
申请号：US16941339
申请日：2020-07-28
Applicant: NVIDIA Corporation
Inventor： Wei Yang , Christopher Jason Paxton , Yu-Wei Chao , Dieter Fox
Abstract: A robotic control system directs a robot to take an object from a human grasp by obtaining an image of a human hand holding an object, estimating the pose of the human hand and the object, and determining a grasp pose for the robot that will not interfere with the human hand. In at least one example, a depth camera is used to obtain a point cloud of the human hand holding the object. The point cloud is provided to a deep network that is trained to generate a grasp pose for a robotic gripper that can take the object from the human's hand without pinching or touching the human's fingers.
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公开(公告)号：US20220032454A1
公开(公告)日：2022-02-03
申请号：US16941339
申请日：2020-07-28
Applicant: NVIDIA Corporation
Inventor： Wei Yang , Christopher Jason Paxton , Yu-Wei Chao , Dieter Fox
Abstract: A robotic control system directs a robot to take an object from a human grasp by obtaining an image of a human hand holding an object, estimating the pose of the human hand and the object, and determining a grasp pose for the robot that will not interfere with the human hand. In at least one example, a depth camera is used to obtain a point cloud of the human hand holding the object. The point cloud is provided to a deep network that is trained to generate a grasp pose for a robotic gripper that can take the object from the human's hand without pinching or touching the human's fingers.
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