Robotic manipulation challenges, such as gras** and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

The incredible feats of athleticism demonstrated by humans are made possible in part by a
vast repertoire of general-purpose motor skills, acquired through years of practice and …

Synthesizing graceful and life-like behaviors for physically simulated characters has been a
fundamental challenge in computer animation. Data-driven methods that leverage motion …

In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

Language provides a way to break down complex concepts into digestible pieces. Recent
works in robot imitation learning use language-conditioned policies that predict actions …

Acquiring a diverse repertoire of general-purpose skills remains an open challenge for
robotics. In this work, we propose self-supervising control on top of human teleoperated play …

Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …

Abstract Theory of mind (ToM) broadly refers to humans' ability to represent the mental
states of others, including their desires, beliefs, and intentions. We design a Theory of Mind …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …