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 …

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 …

Abstract Knowledge from animals and humans inspires robotic innovations. Numerous
efforts have been made to achieve agile locomotion in quadrupedal robots through classical …

Several earlier studies have shown impressive control performance in complex robotic
systems by designing the controller using a neural network and training it with model-free …

Reinforcement learning (RL) for bipedal locomotion has recently demonstrated robust gaits
over moderate terrains using only proprioceptive sensing. However, such blind controllers …

Quadruped robots have shown remarkable mobility on various terrains through
reinforcement learning. Yet, in the presence of sparse footholds and risky terrains such as …

Now more than ever, researchers are rethinking the way robots are designed and controlled—
from the algorithms that govern their actions to the very atomic structure of the materials they …

Transferring human motion skills to humanoid robots remains a significant challenge. In this
study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid …

Legged robots have achieved impressive feats in dynamic locomotion in challenging
unstructured terrain. However, in entertainment applications, the design and control of these …