Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

This work tackles the problem of semi-supervised learning of image classifiers. Our main
insight is that the field of semi-supervised learning can benefit from the quickly advancing …

Unsupervised visual representation learning remains a largely unsolved problem in
computer vision research. Among a big body of recently proposed approaches for …

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen
classes with the help of only a few labeled examples. A recent regularization technique …

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw
sensory inputs, but have yet to achieve the kind of broad generalization and applicability …

Robot learning has emerged as a promising tool for taming the complexity and diversity of
the real world. Methods based on high-capacity models, such as deep networks, hold the …

We present OP3, a framework for model-based reinforcement learning that acquires object
representations from raw visual observations without supervision and uses them to predict …

A video prediction model that generalizes to diverse scenes would enable intelligent agents
such as robots to perform a variety of tasks via planning with the model. However, while …

We demonstrate model-based, visual robot manipulation of deformable linear objects. Our
approach is based on a state-space representation of the physical system that the robot …