We construct an unsupervised learning model that achieves nonlinear disentanglement of
underlying factors of variation in naturalistic videos. Previous work suggests that …

Biological intelligence is remarkable in its ability to produce complex behaviour in many
diverse situations through data efficient, generalisable and transferable skill acquisition. It is …

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked
processes of reinforcement learning: collecting informative experience and inferring optimal …

Robotics has long been a field riddled with complex systems architectures whose modules
and connections, whether traditional or learning-based, require significant human expertise …

Complex adaptive agents consistently achieve their goals by solving problems that seem to
require an understanding of causal information, information pertaining to the causal …

While there has been substantial success for solving continuous control with actor-critic
methods, simpler critic-only methods such as Q-learning find limited application in the …

In policy learning for robotic manipulation, sample efficiency is of paramount importance.
Thus, learning and extracting more compact representations from camera observations is a …

Recent advances in unsupervised learning for object detection, segmentation, and tracking
hold significant promise for applications in robotics. A common approach is to frame these …

Vision-based reinforcement learning (RL) is a generalizable way to control an agent
because it is agnostic of specific hardware configurations. As visual observations are highly …

To address real world decision problems in reinforcement learning, it is common to train a
policy in a simulator first for safety. Unfortunately, the sim-real gap hinders effective …