Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …

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 …

This paper presents a comprehensive study on using deep reinforcement learning (RL) to
create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single …

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 …

Parkour is a grand challenge for legged locomotion that requires robots to overcome various
obstacles rapidly in complex environments. Existing methods can generate either diverse …

Deep reinforcement learning is a promising approach to learning policies in uncontrolled
environments that do not require domain knowledge. Unfortunately, due to sample …

Recent years have seen a surge in commercially-available and affordable quadrupedal
robots, with many of these platforms being actively used in research and industry. As the …

Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …