  Learning from Demonstrations, the field that proposes to learn robot behavior
models from data, is gaining popularity with the emergence of deep generative
models. Although the problem has been studied for years under names such as
Imitation Learning, Behavioral Cloning, or Inverse Reinforcement Learning,
classical methods have relied on models that don't capture complex data
distributions well or don't scale well to large numbers of demonstrations. In
recent years, the robot learning community has shown increasing interest in
using deep generative models to capture the complexity of large datasets. In
this survey, we aim to provide a unified and comprehensive review of the last
year's progress in the use of deep generative models in robotics. We present
the different types of models that the community has explored, such as
energy-based models, diffusion models, action value maps, or generative
adversarial networks. We also present the different types of applications in
which deep generative models have been used, from grasp generation to
trajectory generation or cost learning. One of the most important elements of
generative models is the generalization out of distributions. In our survey, we
review the different decisions the community has made to improve the
generalization of the learned models. Finally, we highlight the research
challenges and propose a number of future directions for learning deep
generative models in robotics.
