BAT: Balancing Agility and Stability via Online Policy Switching for Long-Horizon Whole-Body Humanoid Control
BAT is an online policy-switching framework that balances agile and stable whole-body control for long tasks by dynamically choosing between two complementary controllers: a decoupled, stable policy (πD) and a coupled, agile policy (πC). It uses hierarchical reinforcement learning guided by option evaluation to select the best controller based on the current motion context.