  Behavioral cloning, or more broadly, learning from demonstrations (LfD) is a
priomising direction for robot policy learning in complex scenarios. Albeit
being straightforward to implement and data-efficient, behavioral cloning has
its own drawbacks, limiting its efficacy in real robot setups. In this work, we
take one step towards improving learning from demonstration algorithms by
leveraging implicit energy-based policy models. Results suggest that in
selected complex robot policy learning scenarios, treating supervised policy
learning with an implicit model generally performs better, on average, than
commonly used neural network-based explicit models, especially in the cases of
approximating potentially discontinuous and multimodal functions.
