BAT: Balancing Agility and Stability via Online Policy Switching for Long-Horizon Whole-Body Humanoid Control

TL;DR

This paper introduces BAT, an online policy-switching framework that dynamically balances agility and stability in long-horizon humanoid control by integrating two complementary RL controllers.

Contribution

BAT is a novel framework combining hierarchical RL and option-aware VQ-VAE for dynamic policy switching in humanoid robots.

Findings

BAT outperforms prior methods in diverse long-horizon tasks.

The framework enables versatile loco-manipulation in real-world experiments.

Dynamic policy switching improves robustness and adaptability.

Abstract

Despite recent advances in control, reinforcement learning, and imitation learning, developing a unified framework that can achieve agile, precise, and robust whole-body behaviors, particularly in long-horizon tasks, remains challenging. Existing approaches typically follow two paradigms: coupled whole-body policies for global coordination and decoupled policies for modular precision. However, without a systematic method to integrate both, this trade-off between agility, robustness, and precision remains unresolved. In this work, we propose BAT, an online policy-switching framework that dynamically selects between two complementary whole-body RL controllers to balance agility and stability across different motion contexts. Our framework consists of two complementary modules: a switching policy learned via hierarchical RL with an expert guidance from sliding-horizon policy pre-evaluation, and an option-aware VQ-VAE that predicts option preference from discrete motion token sequences for improved generalization. The final decision is obtained via confidence-weighted fusion of two modules. Extensive simulations and real-world experiments on the Unitree G1 humanoid robot demonstrate that BAT enables versatile long-horizon loco-manipulation and outperforms prior methods across diverse tasks.