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Patent Agency Ranking
公开(公告)号：US11345030B2
公开(公告)日：2022-05-31
申请号：US16424025
申请日：2019-05-28
Applicant: Intel Corporation
Inventor： Venkataraman Natarajan , Gagan Acharya , Ramya M. , Amit Sudhir Baxi , Arjun K. G. , Shagaya Mageshkumar Vincent
Abstract: Methods and apparatus for complex assembly via autonomous robots using reinforcement learning action primitives are disclosed. An example apparatus includes a construction manager and a movement manager. The construction manager is to determine sequences of reinforcement learning (RL) action primitives based on object location goals and associated assembly goals determined for respective ones of objects depicted in an imaged assembly of objects. The movement manager is to command a robot to construct a physical assembly of objects based on the sequences of RL action primitives. The physical assembly of objects is to correspond to the imaged assembly of objects.
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公开(公告)号：US12263599B2
公开(公告)日：2025-04-01
申请号：US17213741
申请日：2021-03-26
Applicant: Intel Corporation
Inventor： Venkat Natarajan , Arjun Kg , Gagan Acharya , Amit Sudhir Baxi , Rita H. Wouhaybi , Wen-Ling Margaret Huang
Abstract: Various aspects of methods, systems, and use cases include techniques for training or using a model to control a robot. A method may include identifying a set of action primitives applicable to a set of robots, receiving information corresponding to a task (e.g., a collaborative task), and determining at least one action primitive based on the received information. The method may include training a model to control operations of at least one robot of the set of robots using the received information and the at least one action primitive.
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公开(公告)号：US20210302956A1
公开(公告)日：2021-09-30
申请号：US17210886
申请日：2021-03-24
Applicant: Intel Corporation
Inventor： Susruth Sudhakaran , Gagan Acharya , Amit Baxi , Dave Cavalcanti , Mark Eisen , Ramya M , Javier Perez-Ramirez , Shagaya Madeshkumar Vincent
IPC: G05D1/00 , H04B17/309 , H04B17/391 , G05D1/02
Abstract: Techniques are disclosed to facilitate multi-agent path planning and to enable navigation for robotics systems to be more resilient to wireless network related issues. The discussed techniques include enhancing path-planning algorithms to consider wireless Quality of Service (QoS) metrics for the identification of planned multi-agent paths. Moreover, the techniques include the compensation of communication and computational latencies to enable offloading of time-sensitive navigation workloads to network infrastructure components.
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公开(公告)号：US20210201183A1
公开(公告)日：2021-07-01
申请号：US17201443
申请日：2021-03-15
Applicant: Intel Corporation
Inventor： Venkataraman Natarajan , Gagan Acharya
Abstract: The present disclosure provides describes to train a multi policy ML model to control robots in a multi-robot system in collaborating to perform a task. For example, trajectories associated with manipulating an object to perform the collaborative task can be determined and an ML model trained to output control actions for the robots in the multi-robot system to collaborate to complete the task.
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5.发明申请公开(公告)号：US20190275671A1
公开(公告)日：2019-09-12
申请号：US16424025
申请日：2019-05-28
Applicant: Intel Corporation
Inventor： Venkataraman Natarajan , Gagan Acharya , Ramya M. , Amit Sudhir Baxi , Arjun K.G. , Shagaya Mageshkumar Vincent
Abstract: Methods and apparatus for complex assembly via autonomous robots using reinforcement learning action primitives are disclosed. An example apparatus includes a construction manager and a movement manager. The construction manager is to determine sequences of reinforcement learning (RL) action primitives based on object location goals and associated assembly goals determined for respective ones of objects depicted in an imaged assembly of objects. The movement manager is to command a robot to construct a physical assembly of objects based on the sequences of RL action primitives. The physical assembly of objects is to correspond to the imaged assembly of objects.
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