2026
CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
RA-L 2026
Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly shaped objectives. In this work, we propose a structured reward shaping framework that leverages model-based trajectory ge