Learned locomotion policies can rapidly adapt to diverse environments similar to those
experienced during training but lack a mechanism for fast tuning when they fail in an out-of …

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 …

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 …

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 …

Deep reinforcement learning can enable robots to autonomously acquire complex behaviors
such as legged locomotion. However, RL in the real world is complicated by constraints on …

The community in legged robotics focuses on bio-inspired robots, although there are some
human inventions that nature could not recreate. One of the most significant examples is the …

Learning complex locomotion and manipulation tasks presents significant challenges, often
requiring extensive engineering of, eg, reward functions or curricula to provide meaningful …

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 …

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