This work proposes Dual-Process Atomic Skill Learning (DASL), a novel asynchronous hierarchical imitation learning framework that decouples slow semantic reasoning from fast, real-time motion control and mitigates the skill codebook interference problem common in joint training paradigms.
Abstract
Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. However, generalizing to multi-step compositional tasks remains a significant challenge. While hierarchical approaches attempt to address this by decomposing tasks into atomic skills, existing methods often suffer from training instability and codebook collapse due to the tight coupling between high-level skill reasoning and low-level action generation in joint training paradigms. Inspired by the Dual-Process Theory of cognition, we propose Dual-Process Atomic Skill Learning (DASL), a novel asynchronous hierarchical imitation learning framework that decouples slow semantic reasoning from fast, real-time motion control. DASL comprises a Slow-Frequency Policy that predicts interpretable, discrete skills via Vector Quantization, and a High-Frequency Policy that leverages a latent diffusion model and a Decision Transformer to generate precise actions conditioned on these latent skills. By asynchronously coordinating these modules and utilizing diffusion to structure the latent space, our framework mitigates the skill codebook interference problem common in joint training paradigms. Evaluations across simulation benchmarks and experiment demonstrate that DASL significantly outperforms state-of-the-art baselines, excelling in skill acquisition and compositional generalization to unseen instructions. GitHub page: https://github.com/Hatakekaka/DASL
To prevent the text-to-motion skill from exploiting shortcut pathways instead of learning language semantics, motion intuition distillation is introduced to ground text-to-motion generation in language semantics and a task-guidance module that dynamically adjusts actions based on high-level language instructions is introduced.
Yueyi Zhang, Ming Gong, Linpu He et al.· 0 citations
Vision-language-action (VLA) models are commonly adapted to downstream manipulation tasks via supervised fine-tuning (SFT) or online reinforcement learning (RL) post-training. SFT is prone to distribution mismatch, and existing RL approaches typically apply a single, uniform update strategy to all model components, ignoring their distinct functional roles. We propose TEMPO, a semantic-action decoupled, two-timescale RL post-training framework for VLA models. TEMPO freezes the pretrained vision-language backbone to preserve general semantic representations, and restricts adaptation to two components with dedicated RL optimization loops: the semantic projection layer and the low-level action expert. We update them at different rates--the semantic projection layer infrequently, to keep the latent action stable, and the action expert frequently, to rapidly incorporate control feedback from online interaction. This decoupling RL fine-tuning strategy prevents fast policy updates from destabilizing high-level semantic representations while still allowing the action expert to learn efficiently from online feedback. Experiments on the CALVIN benchmark and real-world manipulation tasks demonstrate that TEMPO consistently outperforms both pretrained state-of-the-art VLA models and the RL post-training baseline, while reaching and maintaining higher evaluation rewards on two real-world tasks.
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.
Ziru Wang, Long Qian, Hao-Wen Sun et al.· IEEE Transactions on Automat...· 0 citations
HyMeS, a hybrid learning framework that leverages the reasoning and memory-management capabilities of coding agents to steer a Markovian VLA for memory-dependent manipulation, is proposed, enabling data-efficient compositional generalization.
Yunhao Zhao, Zhenyang Ni, Haoyang Chen et al.· 0 citations
Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot learning. For efficient skill transfer to real robots, learned skills must generalize across tasks and domains, remain robust to visual and dynamic perturbations, and be efficient enough for practical deployment. However, existing methods typically satisfy only a subset of these properties, as they capture either high-level semantic intent (what) or low-level motion dynamics (how). This incomplete skill transfer yields weak priors for policy learning, thereby demanding substantial in-domain data for downstream adaptation. To address these challenges, we introduce BooST, a two-stage framework that explicitly bridges semantics and motions to satisfy all three desiderata. BooST first leverages a cross-modal VQ-VAE to capture both semantic intent and motion dynamics, yielding a unified skill representation. It then distills this representation into a lightweight policy for efficient downstream adaptation to new tasks. Extensive experiments across simulation and real-robot settings demonstrate that BooST achieves superior few-shot adaptation, cross-domain skill transfer, and robustness to dynamic visual distractors, while maintaining a lightweight yet expressive design suitable for real-world deployment.
Jusuk Lee, Daesol Cho, Jonghu Shin et al.· IEEE Robotics and Automation...· 0 citations
Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making. Existing methods often coordinate pretrained skills with scripted planners, finite-state machines or task-specific model-free policies, restricting their ability to handle complex task sequences. To address this limitation, we propose \textbf{LUCID}, a hierarchical model-based reinforcement learning framework that plans over reusable skills through imagined rollouts of a learned dynamics model. LUCID first trains a structured latent-conditioned low-level policy via adversarial imitation and then freezes it while jointly learning a high-level policy and macro-dynamics world model. The world model predicts the temporally extended state transitions induced by latent decisions, enabling high-level policy optimization through imagined rollouts. We evaluate our framework across various simulated multi-object rearrangement scenarios. Experimental results show that LUCID improves the full-task success and partial-completion rates compared to prior baseline methods, demonstrating its effectiveness in complex sequential loco-manipulation tasks.
Cheng Guo, Mingzhe Ni, A. Cangelosi et al.· 0 citations
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