Google Inc.

Zhenyu Tan

Peng Xu

Sergey Levine

Jie Tan

Sehoon Ha

Techniques are disclosed that enable training a plurality of policy networks, each policy network corresponding to a disparate robotic training task, using a mobile robot in a real world workspace. Various implementations include selecting a training task based on comparing a pose of the mobile robot to at least one parameter of a real world training workspace. For example, the training task can be selected based on the position of a landmark, within the workspace, relative to the pose. For instance, the training task can be selected such that the selected training task moves the mobile robot towards the landmark.

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Sunnyvale, CA, United States of America

Zhenyu Tan