# Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Data

arXiv: https://arxiv.org/abs/2511.17373 · HTML: https://mlsanity.com/paper/2511.17373/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2025.

## Built on by (most cited first, 11 of 11)

- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos](https://mlsanity.com/paper/2602.02473/) (2026)
- [TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control](https://mlsanity.com/paper/2602.07439/) (2026)
- [Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Control](https://mlsanity.com/paper/2603.27756/) (2026)
- [Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors](https://mlsanity.com/paper/2605.22272/) (2026)
- [Direct Dynamic Retargeting for Humanoid Imitation Learning from Videos](https://mlsanity.com/paper/2605.23762/) (2026)
- [PDF-HR: Pose Distance Fields for Humanoid Robots](https://mlsanity.com/paper/2602.04851/) (2026)
- [Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots](https://mlsanity.com/paper/2604.19104/) (2026)
- [Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking](https://mlsanity.com/paper/2605.23733/) (2026)
- [ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids](https://mlsanity.com/paper/2609.15988/) (2026)
- [EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation](https://mlsanity.com/paper/2609.37181/) (2026)

## Abstract (arXiv)

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.