Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control

Language-conditioned imitation learning must discover reusable skills from demonstrations to generalize to multi-step instructions. Joint training of discrete skill hierarchies can produce concentrated codebook usage and unstable skill representations. We propose Dual-Process Atomic Skill Learning (DASL), which independently parameterizes the semantic update interval and low-level trajectory horizon, supervises each shared skill with actions across its interval, and regularizes skill-conditioned latent trajectories with a diffusion module used only during training. A slow Option Transformer predicts vector-quantized skills, while a Decision Transformer generates actions from full-rate observations without diffusion sampling at inference. Evaluations on LOReL, Franka Kitchen, a six-task CALVIN-D state-based protocol, BabyAI, and a real robot demonstrate improved skill acquisition and compositional performance in these settings. Direct codebook diagnostics and an isolated interval-supervision ablation support the proposed training mechanism, while the deterministic inference path preserves efficient control.