DualSkill: Unifying Discrete Stability and Continuous Flexibility for Embodied Control

Abstract

Learning to execute complex, multi-stage tasks requires skill representations that are both compositionally stable and adaptive in execution. Existing hierarchical approaches often face a fundamental trade-off: continuous skills suffer from representational drift due to unconstrained embedding boundaries, while discrete skills exhibit limited expressivity because their deterministic selection cannot capture the multi-modal nuances required for adaptive execution. This tension makes it difficult to achieve reliable composition and adaptive control within a single framework. To address this, we propose DualSkill, a hierarchical framework that learns stable hard skill primitives and builds adaptive soft skills from them. Specifically, DualSkill acquires discrete hard skills via vector quantization with motion-aware distillation, yielding robust and reusable motion primitives that provide structural anchors for skill composition. Conditioned on these primitives, soft skills are modeled as probabilistic continuous mixtures that adapt skill execution while preserving temporal consistency. DualSkill then predicts future skill intentions autoregressively and decodes them into precise low-level actions. We support DualSkill with both theoretical guarantees on its skill representation and extensive experiments across diverse simulation benchmarks and a real-world robotic platform, showing that it outperforms strong baselines and improves generalization. Note to Practitioners—This paper was motivated by the need for robots to execute complex, multi-step tasks in dynamic environments such as homes, warehouses, and factories. In practice, control systems often struggle to balance modular, reusable skills with smooth transitions, leading to unstable or inefficient behavior when task conditions change. Existing approaches typically force a trade-off: either continuous skills that suffer from representational drift or discrete libraries that result in inflexible behavior. This paper presents DualSkill, a hierarchical framework that bridges this gap by decomposing behaviors into stable hard skills for structural reliability and adaptive soft skills for smooth execution. We validate that DualSkill significantly reduces failure rates in complex manipulation tasks on both simulated benchmarks and physical robots. However, the system still relies on structured training data, which may limit its initial deployment in highly unstructured environments. In the future, DualSkill could be applied to mobile robots and human-robot collaboration, further leveraging its flexible and robust framework for real-world tasks.