  This paper presents an innovative method for humanoid robots to acquire a
comprehensive set of motor skills through reinforcement learning. The approach
utilizes an achievement-triggered multi-path reward function rooted in
developmental robotics principles, facilitating the robot to learn gross motor
skills typically mastered by human infants within a single training phase. The
proposed method outperforms standard reinforcement learning techniques in
success rates and learning speed within a simulation environment. By leveraging
the principles of self-discovery and exploration integral to infant learning,
this method holds the potential to significantly advance humanoid robot motor
skill acquisition.
