We investigate the use of prior knowledge of human and animal movement to learn reusable
locomotion skills for real legged robots. Our approach builds upon previous work on …

The agility of animals, particularly in complex activities such as running, turning, jumping,
and backflipping, stands as an exemplar for robotic system design. Transferring this suite of …

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation
learning. The difficulty of collecting task demonstrations and training policies for humanoids …

Learning to control complex bodies and reuse learned behaviors is a longstanding
challenge in continuous control. We study the problem of learning reusable humanoid skills …

We present SLoMo: a first-of-its-kind framework for transferring skilled motions from casually
captured “in-the-wild” video footage of humans and animals to legged robots. SLoMo works …

This letter presents a new learning framework that leverages the knowledge from imitation
learning, deep reinforcement learning, and control theories to achieve human-style …

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 …

Can we enable humanoid robots to generate rich, diverse, and expressive motions in the
real world? We propose to learn a whole-body control policy on a human-sized robot to …

Simulated humanoids are an appealing research domain due to their physical capabilities.
Nonetheless, they are also challenging to control, as a policy must drive an unstable …

Reproducing the diverse and agile locomotion skills of animals has been a longstanding
challenge in robotics. While manually-designed controllers have been able to emulate many …