Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

In the past decade, we have witnessed the rise of deep learning to dominate the field of
artificial intelligence. Advances in artificial neural networks alongside corresponding …

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 …

Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring
precise eye-muscle coordination and movement. Getting robots to do the same task requires …

Animals are capable of precise and agile locomotion using vision. Replicating this ability
has been a long-standing goal in robotics. The traditional approach has been to decompose …

Humanoid robots that can autonomously operate in diverse environments have the potential
to help address labor shortages in factories, assist elderly at home, and colonize new …

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for
legged robots. We present an end-to-end learned controller that achieves record agility for …

An attached arm can significantly increase the applicability of legged robots to several
mobile manipulation tasks that are not possible for the wheeled or tracked counterparts. The …

Autonomous wheeled-legged robots have the potential to transform logistics systems,
improving operational efficiency and adaptability in urban environments. Navigating urban …

Learned locomotion policies can rapidly adapt to diverse environments similar to those
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …