Legged robots have enormous potential in their range of capabilities, from navigating
unstructured terrains to high-speed running. However, designing robust controllers for highly …

Learning agile skills is one of the main challenges in robotics. To this end, reinforcement
learning approaches have achieved impressive results. These methods require explicit task …

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 …

Developing both robust and agile locomotion skills for legged robots is non-trivial. In this
work, we present the first blind locomotion system capable of traversing challenging terrains …

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 …

Motor neuroscience and physics-based character animation (PBCA) approach human and
humanoid control from different perspectives. The primary goal of PBCA is to control the …

Using learned reward functions (LRFs) as a means to solve sparse-reward reinforcement
learning (RL) tasks has yielded some steady progress in task-complexity through the years …

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its
application to the real-world robotics domain. Guiding the learning process with additional …

Quadrupedal robots resemble the physical ability of legged animals to walk through
unstructured terrains. However, designing a controller for quadrupedal robots poses a …

Reinforcement Learning (RL) has seen many recent successes for quadruped robot control.
The imitation of reference motions provides a simple and powerful prior for guiding solutions …