FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems
FAITH introduces a model-free reinforcement learning method that separates safety enforcement from task optimization. It approximates an optimal safety value and uses a learned feedforward filter to correct actions, allowing the task policy to optimize for reward while the filter ensures feasibility. This enables high performance in complex, high-dimensional systems like humanoid robots.