  Self-supervised skill learning aims to acquire useful behaviors that leverage
the underlying dynamics of the environment. Latent variable models, based on
mutual information maximization, have been successful in this task but still
struggle in the context of robotic manipulation. As it requires impacting a
possibly large set of degrees of freedom composing the environment, mutual
information maximization fails alone in producing useful and safe manipulation
behaviors. Furthermore, tackling this by augmenting skill discovery rewards
with additional rewards through a naive combination might fail to produce
desired behaviors. To address this limitation, we introduce SLIM, a
multi-critic learning approach for skill discovery with a particular focus on
robotic manipulation. Our main insight is that utilizing multiple critics in an
actor-critic framework to gracefully combine multiple reward functions leads to
a significant improvement in latent-variable skill discovery for robotic
manipulation while overcoming possible interference occurring among rewards
which hinders convergence to useful skills. Furthermore, in the context of
tabletop manipulation, we demonstrate the applicability of our novel skill
discovery approach to acquire safe and efficient motor primitives in a
hierarchical reinforcement learning fashion and leverage them through planning,
significantly surpassing baseline approaches for skill discovery.
