BAT: Balancing Agility and Stability via Online Policy Switching for Long-Horizon Whole-Body Humanoid Control
arXiv:2604.01064v1 Announce Type: new 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 consi
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2604.01064 [cs.RO]
(or arXiv:2604.01064v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2604.01064
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Donghoon Baek [view email] [v1] Wed, 1 Apr 2026 16:03:27 UTC (4,124 KB)
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