Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Bipedal robots are gaining global recognition due to their potential applications and
advancements in artificial intelligence, particularly through Deep Reinforcement Learning …

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 …

In this work, we present MoConVQ, a novel unified framework for physics-based motion
control leveraging scalable discrete representations. Building upon vector quantized …

Abstract Model-free reinforcement learning (RL) for legged locomotion commonly relies on a
physics simulator that can accurately predict the behaviors of every degree of freedom of the …

A necessary capability for humanoid robots is the ability to stand and walk while rejecting
natural disturbances. Recent progress has been made using sim-to-real reinforcement …

This work presents a hierarchical framework for bipedal locomotion that combines a
Reinforcement Learning (RL)-based high-level (HL) planner policy for the online generation …

Recently, work on reinforcement learning (RL) for bipedal robots has successfully learned
controllers for a variety of dynamic gaits with robust sim-to-real demonstrations. In order to …

This article focuses on vision-based planning strategies for legged robots that separate
locomotion planning into foothold selection and pose adaptation. Current pose adaptation …

When legged robots impact their environment, they undergo large changes in their velocities
in a small amount of time. Measuring and applying feedback to these velocities is …