A method of training a machine learning model to be utilized in performing a first robotic manipulation task using a real robot, the method implemented by one or more processors and 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 (133A1), and simulated training example output (133A2) that is based on whether the 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 first real robot manipulates a real object, wherein the real training example includes: real training example input (149A1), and real training example output (149A2) 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 the 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 (S1 LOSS) based on comparing the first predicted output to the simulated training example output (133A2) 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 (R2 LOSS) based on comparing the second predicted output to the real training example output (149A2) 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. 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 (133A1), and simulated training example output (133A2) that is based on whether the first robotic manipulation task was successful for the simulated robotic manipulation episode; simulated training example input (133A1), and simulated training example output (133A2) that is based on whether the 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 first real robot manipulates a real object, wherein the real training example includes: real training example input (149A1), and real training example output (149A2) 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; real training example input (149A1), and real training example output (149A2) 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 the 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 (S1 LOSS) based on comparing the first predicted output to the simulated training example output (133A2) 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 (R2 LOSS) based on comparing the second predicted output to the real training example output (149A2) 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.