Underactuated bipedal robot balance control based on TD3 reinforcement learning

This study proposes a reinforcement learning framework for dynamic balance control in underactuated bipedal robots. Leveraging Gazebo simulations and a hybrid reward function, the framework integrates proprioceptive sensing and PD-controlled joint actions to enable stable locomotion. A decaying noise strategy balances exploration and exploitation, guiding the robot from instability to reliable target navigation. Training over 100,000 iterations demonstrated policy convergence, with improved rewards and task completion times.