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

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 …

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 …

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

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 …

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 …

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 …

In this work, we propose a method to generate reduced-order model reference trajectories
for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real …

Robots operating in human environments require a diverse set of skills, including slow and
fast walking, turning, side-stepping, and more. However, developing robot controllers …

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