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.

翻译：尽管控制、强化学习和模仿学习领域已取得显著进展，但开发能够实现敏捷、精确且鲁棒的全身行为（尤其在长时域任务中）的统一框架仍具挑战性。现有方法通常遵循两种范式：用于全局协调的耦合全身策略与用于模块化精确性的解耦策略。然而，由于缺乏系统化的融合方法，敏捷性、鲁棒性与精确性之间的权衡问题仍未解决。本文提出BAT——一种在线策略切换框架，通过动态选择两个互补的全身强化学习控制器，在不同运动场景中平衡敏捷性与稳定性。该框架包含两个互补模块：基于分层强化学习与滑动时域策略预评估专家引导训练的切换策略，以及通过离散运动标记序列预测选项偏好以提升泛化能力的选项感知VQ-VAE。最终决策通过两个模块的置信度加权融合生成。在宇树G1人形机器人上的大规模仿真与真实世界实验表明，BAT能够实现多样化的长时域移动操作任务，并在各类任务中均优于既有方法。