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 …

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 …

Learning provides a powerful tool for vision-based navigation, but the capabilities of
learning-based policies are constrained by limited training data. If we could combine data …

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 …

Learning a locomotion policy for quadruped robots has traditionally been constrained to a
specific robot morphology, mass, and size. The learning process must usually be repeated …

This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled
bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a …

While reinforcement learning has achieved remarkable successes in several domains, its
real-world application is limited due to many methods failing to generalise to unfamiliar …

Bipedal robots are garnering increasing global attention due to their potential applications
and advancements in artificial intelligence, particularly in Deep Reinforcement Learning …

Replicating a user's pose from only wearable sensors is important for many AR/VR
applications. Most existing methods for motion tracking avoid environment interaction apart …

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