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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：US20220100184A1
公开(公告)日：2022-03-31
申请号：US17546158
申请日：2021-12-09
Applicant: Intel Corporation
Inventor： Venkataraman Natarajan , Amala Sonny
IPC: G05B19/418 , B25J5/00 , B25J9/16
Abstract: Techniques are disclosed to perform task allocation for autonomous systems by implementing machine-learning to perform task allocation to Autonomous Mobile Robots (AMRs) in an environment. The disclosed techniques also provide for enhanced path planning and the identification of AMR health and failure prediction to further improve upon task allocation and system efficiency.
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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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公开(公告)号：US10448415B2
公开(公告)日：2019-10-15
申请号：US15646504
申请日：2017-07-11
Applicant: INTEL CORPORATION
Inventor： Venkataraman Natarajan , Apoorv Vyas , Jaroslaw J. Sydir , Kumar Ranganathan
Abstract: Techniques disclosed for accurately predicting the occurrence of anomalous sensor readings within a sensor network and advantageously using these predictions to limit the amount of power used by relay nodes within the sensor network. Some examples analyze spatial and temporal characteristics of anomalous sensor readings to predict future occurrences. In these examples, the relay nodes operate in a reduced power mode for periods of time in which anomalous sensor readings are not predicted to occur. Also, in these examples, only relay nodes in a path between a sensor predicting an anomalous reading and a gateway of the sensor network operate in full power mode. This feature allows other relay nodes to remain in the reduced power mode even when an anomalous sensor reading is predicted elsewhere in the sensor network.
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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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