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 …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

Performing agile navigation with four-legged robots is a challenging task because of the
highly dynamic motions, contacts with various parts of the robot, and the limited field of view …

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 …

Simulation-based reinforcement learning approaches are leading the next innovations in
legged robot control. However, the resulting control policies are still not applicable on soft …

Legged robots that can operate autonomously in remote and hazardous environments will
greatly increase opportunities for exploration into underexplored areas. Exteroceptive …

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for
legged robots. We present an end-to-end learned controller that achieves record agility for …

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 …

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance,
and planning of the underactuated dynamics of the system. Reliably optimizing for such …

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 …