Learning to Walk Like Humans Do: A Developmental Approach to Locomotion in Deep Reinforcement Learning

Abstract

Humans learn to stand and walk within a matter of months, exploiting a reliable sequence of postural milestones that partitions the full motor configuration space into a series of stable, tractable subproblems. In contrast, humanoid agents trained with standard reinforcement learning (RL) enjoy no such structure, confronting the full complexity of locomotion at once and facing an enormous exploration burden in high-dimensional continuous control. To address this, we propose a hierarchical reinforcement learning (HRL) framework that leverages the structure of human motor development by training each key postural transition—prone-to-crawl, crawl-to-kneel, kneel-to-lunge, lunge-to-stand and stand-to-walk—as a distinct low-level policy, thus imposing a developmental prior over the skill set. A high-level policy then learns to coordinate these motor skills via the Value Function Spaces (VFS) framework. We first validate our approach in LunarLander and BipedalWalker before applying it to Humanoid, where developmental structure directly informs the skill decomposition. Against flat RL baselines and HRL methods that discover structure from experience, our approach achieves upright posture in over 90% of evaluation episodes and completes the full developmental sequence in over 85%, while all baselines plateau at intermediate configurations. Our findings suggest that developmentally grounded structural priors substantially reduce the exploration burden of complex locomotion learning, enabling reliable postural progression where reward engineering, intrinsic motivation and emergent hierarchies fall short.