This work introduces DiffuseLoco, a framework for training multi-skill diffusion-based policies
for dynamic legged locomotion from offline datasets, enabling real-time control of diverse …

Learning-based methods have improved locomotion skills of quadruped robots through
deep reinforcement learning. However, the sim-to-real gap and low sample efficiency still …

This letter presents a control framework that combines model-based optimal control and
reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our …

Optimal Control for legged robots has gone through a paradigm shift from position-based to
torque-based control, owing to the latter's compliant and robust nature. In parallel to this …

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 …

Understanding the gap between simulation and reality is critical for reinforcement learning
with legged robots, which are largely trained in simulation. However, recent work has …

Deep reinforcement learning produces robust locomotion policies for legged robots over
challenging terrains. To date, few studies have leveraged model-based methods to combine …

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

Learning controllers that reproduce legged locomotion in nature has been a longtime goal in
robotics and computer graphics. While yielding promising results, recent approaches are not …

Learning to locomote to arbitrary goals on hardware remains a challenging problem for
reinforcement learning. In this paper, we present a hierarchical framework that improves …