CPC B25J 9/161 (2013.01) [B25J 9/163 (2013.01); B25J 9/1671 (2013.01); B25J 9/1697 (2013.01); G05B 13/027 (2013.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/2431 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 7/50 (2017.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] | 20 Claims |
1. A method, comprising:
receiving an input image of a real-word environment captured while a robotic agent is interacting with the real-world environment;
processing the input image of the real-world environment using a trained generator neural network to generate an adapted image from the input image, the trained generator neural network having been trained to adapt images of a randomized simulation of the real-world environment into images of a canonical simulation of the real-world environment;
providing the adapted image as input to a control policy for the robotic agent to select a subsequent action to be performed by the robotic agent; and
controlling the robotic agent to perform the selected subsequent action in the real-world environment.
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