Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Data

TL;DR

This paper introduces AMS, a unified control framework for humanoid robots that combines agility and stability by leveraging heterogeneous data sources and adaptive learning, enabling versatile behaviors from dancing to extreme balance.

Contribution

The paper presents AMS, the first unified framework that integrates dynamic motion tracking and balance control using heterogeneous data and hybrid reward schemes.

Findings

Single policy executes agile and stable motions

Enables zero-shot extreme balance behaviors

Validated on real humanoid robot

Abstract

Humanoid robots are envisioned to perform a wide range of tasks in human-centered environments, requiring controllers that combine agility with robust balance. Recent advances in locomotion and whole-body tracking have enabled impressive progress in either agile dynamic skills or stability-critical behaviors, but existing methods remain specialized, focusing on one capability while compromising the other. In this work, we introduce AMS (Agility Meets Stability), the first framework that unifies both dynamic motion tracking and extreme balance maintenance in a single policy. Our key insight is to leverage heterogeneous data sources: human motion capture datasets that provide rich, agile behaviors, and physically constrained synthetic balance motions that capture stability configurations. To reconcile the divergent optimization goals of agility and stability, we design a hybrid reward scheme that applies general tracking objectives across all data while injecting balance-specific priors only into synthetic motions. Further, an adaptive learning strategy with performance-driven sampling and motion-specific reward shaping enables efficient training across diverse motion distributions. We validate AMS extensively in simulation and on a real Unitree G1 humanoid. Experiments demonstrate that a single policy can execute agile skills such as dancing and running, while also performing zero-shot extreme balance motions like Ip Man's Squat, highlighting AMS as a versatile control paradigm for future humanoid applications.