We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

Performing agile navigation with four-legged robots is a challenging task because of the
highly dynamic motions, contacts with various parts of the robot, and the limited field of view …

Legged locomotion is a complex control problem that requires both accuracy and
robustness to cope with real-world challenges. Legged systems have traditionally been …

Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday
environments. These skills can be assessed on the basis of a system's ability to coordinate …

We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that
enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB …

DribbleBot (Dexterous Ball Manipulation with a Legged Robot) is a legged robotic system
that can dribble a soccer ball under the same real-world conditions as humans. We identify …

Abstract Knowledge from animals and humans inspires robotic innovations. Numerous
efforts have been made to achieve agile locomotion in quadrupedal robots through classical …

We present a universal motion representation that encompasses a comprehensive range of
motor skills for physics-based humanoid control. Due to the high dimensionality of …

We present a system for applying sim2real approaches to “in the wild” scenes with realistic
visuals, and to policies which rely on active perception using RGB cameras. Given a short …

We present SimXR a method for controlling a simulated avatar from information (headset
pose and cameras) obtained from AR/VR headsets. Due to the challenging viewpoint of …