(Originality) The main originality in the work is the full system to go from videos of human demonstrations to a robot policy that can solve a multi-stage task. The authors break this problem up into several pieces. They propose to train an image translation model to translate from human demonstration videos to robot videos to account for differences in morphology. Next, they manually segment the human videos at stage boundaries into instruction images that are used for training a model-based RL agent to reach these instruction images and solve each part of a task in sequence. The authors leverage a latent-space model-based RL algorithm along with a learned classifier that provides rewards for reaching instruction images. They use the classifier confidence to dictate whether additional human labels of success and failure should be solicited, and whether to try and autonomously reset to the start of a stage to try again, in order to limit human intervention in the system. There have already been several works that tackle each of the aforementioned pieces - including using CycleGAN for image translation, latent-space MPC for model-based RL, doing RL with autonomous resets, and using a learned classifier as a reward function in real robot RL along with human queries. The main novelty of the paper is a system that combines all of these approaches together, and demonstrating the efficacy of the system over alternative choices. (Quality) The authors compare against a large set of baselines and ablations, and the empirical evaluations are good. They showcase the value of instruction images, using latent-space planning (compared to planning directly in image space) and the value of stage-wise training and resetting through the classifier (when compared to a method like BC). However, although the tasks shown are multi-stage, the action space seems to have been severely restricted, making the tasks significantly easier. The robot appears to purely move in a 2D vertical plane at a very low rate. While the authors mention that end effector velocity control is used, the robot appears to have just 3 dimensions of control - a delta position in 2 dimensions, and a grasping signal. The quality of the final policies is also pretty poor (this is perhaps due to the frequency of control being low - the policy is very choppy in execution and seems to fail often). (Clarity) The clarity of the paper is sufficient. A few parts of the method could use some more detail. For example, the MPC-CEM subroutine could use some more explanation. (Significance) While the basic motivation is clear - to ease the burden of humans while allowing the system to learn as much as it can on its own from videos of human demonstrations and a modest amount of human labels - there are significant concerns about why the proposed methodology should be used in practice over alternatives. Only a subset of translated CycleGAN translated images are used - the instruction images. Consequently there is no need for the CycleGAN to generate accurate translations - they only need to be accurate at the task segment boundaries. Furthermore, the ease of providing supervision for the CycleGAN is questionable when compared to an alternative such as kinesthetic teaching. The authors mention that only a modest number of human videos are used for traning. In that case, it seems as though kinesthetic teaching could be used to just collect a variety of robot images at stage boundaries. The CycleGan supervision already requires placing items into the robot's hand - it is a small step from there, to moving the robot to the appropriate locations in the environment. It seems to me that not much technical human ability would be required to capture a modest number of frames of the robot in each of the stages, compared to ensuring that diverse and varied robot data is captured for training the full CycleGAN. That being said, the generated robot translations seem to be of surprisingly high quality.