Machine learning methods and apparatus for robotic manipulation and that utilize multi-task domain adaptation

A method implemented by one or more processors, comprising:

• identifying a simulated training example generated based on simulated data, the simulated data generated during performance of a simulated robotic manipulation episode in which a simulated robot manipulates a simulated object in a simulated environment, wherein the simulated training example includes:
• simulated training example input, and
• simulated training example output that is based on whether a first robotic manipulation task was successful for the simulated robotic manipulation episode;
• identifying a real training example generated based on real data, the real data generated during performance of a real robotic manipulation episode in which a real robot manipulates a real object, wherein the real training example includes:
• real training example input, and
• real training example output that is based on whether a second robotic manipulation task was successful for the real robotic manipulation episode, the second robotic manipulation task being distinct from the first robotic manipulation task;
• applying the simulated training example input to a machine learning model;
• generating a first predicted output based on processing of the simulated training example input using the machine learning model;
• generating a first loss based on comparing the first predicted output to the simulated training example output that is based on whether the first robotic manipulation task was successful;
• applying the real training example input to the machine learning model, or to an additional machine learning model that includes a portion of the machine learning model;
• generating a second predicted output based on processing of the real training example input using the machine learning model or the additional machine learning model;
• generating a second loss based on comparing the second predicted output to the real training example output that is based on whether the second robotic manipulation task was successful;
• and
• training at least the portion of the machine learning model based on both the first loss and the second loss.