| CPC G06N 3/008 (2013.01) [B25J 9/163 (2013.01); B25J 9/1697 (2013.01); G06N 20/00 (2019.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/10 (2022.01); G06V 20/70 (2022.01)] | 11 Claims |
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1. A method implemented by one or more processors of a robot, the method comprising:
generating first vision data using one or more first vision components that are connected to the robot,
wherein the first vision data characterizes a portion of an area that the robot is approaching;
determining, based on the first vision data, whether the portion of the area includes a surface that is traversable by the robot;
generating second vision data that characterizes a separate portion of the area,
wherein the second vision data is generated using one or more second vision components that are: separate from the one or more first vision components, and also connected to the robot;
determining, based on the second vision data, whether an additional surface included in the separate portion of the area is traversable by the robot,
wherein determining whether the additional surface that is traversable by the robot is performed using one or more machine learning models, and
wherein the one or more machine learning models are trained using one or more instances of training data that include vision data characterizing one or more particular surfaces and label data characterizing drivability of the one or more particular surfaces; and
causing the robot to operate according to whether the surface and the additional surface are determined to be traversable.
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