Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

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 …

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 …

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 …

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 …

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 …

Teleoperation serves as a powerful method for collecting on-robot data essential for robot
learning from demonstrations. The intuitiveness and ease of use of the teleoperation system …

Quadrupedal robots resemble the physical ability of legged animals to walk through
unstructured terrains. However, designing a controller for quadrupedal robots poses a …

Legged robots have the potential to expand the reach of autonomy beyond paved roads. In
this work, we consider the difficult problem of locomotion on challenging terrains using a …