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 …

Legged robots navigating cluttered environments must be jointly agile for efficient task
execution and safe to avoid collisions with obstacles or humans. Existing studies either …

Identifying the physical properties of the surrounding environment is essential for robotic
locomotion and navigation to deal with non-geometric hazards, such as slippery and …

Legged navigation is typically examined within open-world, off-road, and challenging
environments. In these scenarios, estimating external disturbances requires a complex …

Incorporating a robotic manipulator into a wheel-legged robot enhances its agility and
expands its potential for practical applications. However, the presence of potential instability …

Representation learning and unsupervised skill discovery can allow robots to acquire
diverse and reusable behaviors without the need for task-specific rewards. In this work, we …

Through rigorous research into the mechanical designs and locomotion algorithms of
quadrupedal robots, they have been enabled to agilely traverse a variety of scenarios with …