Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

Abstract Representation learning algorithms are designed to learn abstract features that
characterize data. State representation learning (SRL) focuses on a particular kind of …

For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be
able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to …

The application of deep learning in robotics leads to very specific problems and research
questions that are typically not addressed by the computer vision and machine learning …

State representation learning, or the ability to capture latent generative factors of an
environment is crucial for building intelligent agents that can perform a wide variety of tasks …

A key challenge in complex visuomotor control is learning abstract representations that are
effective for specifying goals, planning, and generalization. To this end, we introduce …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

It is a long-standing problem to find effective representations for training reinforcement
learning (RL) agents. This paper demonstrates that learning state representations with …

Most deep reinforcement learning techniques are unsuitable for robotics, as they require too
much interaction time to learn useful, general control policies. This problem can be largely …

Insertion is a challenging haptic and visual control problem with significant practical value
for manufacturing. Existing approaches in the model-based robotics community can be …