Invention Grant
- Patent Title: Device control using policy training based on task embeddings
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Application No.: US17207281Application Date: 2021-03-19
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Publication No.: US12053886B2Publication Date: 2024-08-06
- Inventor: Stephen Lloyd James , Michael Bloesch , Andrew Davison
- Applicant: Imperial College Innovations Limited
- Applicant Address: GB London
- Assignee: Imperial College Innovations Limited
- Current Assignee: Imperial College Innovations Limited
- Current Assignee Address: GB London
- Agency: EIP US LLP
- Priority: GB 15431 2018.09.21
- Main IPC: B25J9/16
- IPC: B25J9/16 ; G05B19/4155

Abstract:
A control system for a robotic device comprising a task embedding network to receive one or more demonstrations of a task and to generate a task embedding. The task embedding comprises a representation of the task, and each demonstration comprises one or more observations of a performance of the task. The control system includes a control network to receive the task embedding from the task embedding network and to apply a policy to map a plurality of successive observations of the robotic device to respective control instructions for the robotic device. The policy applied by the control network is modulated across the plurality of successive observations of the robotic device using the task embedding from the task embedding network.
Public/Granted literature
- US20210205988A1 TASK EMBEDDING FOR DEVICE CONTROL Public/Granted day:2021-07-08
Information query
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