Large language models (LLMs) have demonstrated exciting progress in acquiring diverse
new capabilities through in-context learning, ranging from logical reasoning to code-writing …

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 …

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 can enable robots to autonomously acquire complex behaviors
such as legged locomotion. However, RL in the real world is complicated by constraints on …

We present CAJun, a novel hierarchical learning and control framework that enables legged
robots to jump continuously with adaptive jumping distances. CAJun consists of a high-level …

Humans interact with objects all the time. Enabling a humanoid to learn human-object
interaction (HOI) is a key step for future smart animation and intelligent robotics systems …

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 …

The robustness of legged locomotion is crucial for quadrupedal robots in challenging
terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged …

Controlled execution of dynamic motions in quadrupedal robots, especially those with
articulated soft bodies, presents a unique set of challenges that traditional methods struggle …