BooST：桥接语义与运动以实现高效的技能迁移
BooST: Bridging Semantics and Motions for Efficient Skill Transfer
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- Seoul National University（首尔大学）
- Georgia Institute of Technology（佐治亚理工学院）
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中文总结 AI 辅助
BooST是桥接语义与运动的两阶段框架，通过跨模态VQ-VAE生成统一技能表示并蒸馏为轻量级策略，在仿真与真实机器人场景下实现了更优的少样本适配、跨领域技能迁移及鲁棒性。
中文摘要 AI 辅助
技能抽象——即学习可复用且具有时间扩展性行为的过程——已成为提升机器人学习中样本效率与泛化性的关键范式。为实现向真实机器人的高效技能迁移，所学技能需能跨任务与领域泛化、对视觉及动态扰动保持鲁棒性，且需具备足够效率以支持实际部署。然而，现有方法通常仅满足部分此类特性，因其仅捕获高层语义意图（即“做什么”）或低层运动动力学（即“如何做”）。这种不完整的技能迁移会为策略学习提供较弱的先验，进而需要大量领域内数据用于下游适配。为应对这些挑战，我们提出BooST，这是一个两阶段框架，明确桥接语义与运动以满足所有三项要求。BooST首先利用跨模态VQ-VAE捕获语义意图与运动动力学，生成统一的技能表示；随后将该表示蒸馏为轻量级策略，以高效适配新任务。在仿真与真实机器人场景下开展的大量实验表明，BooST在少样本适配、跨领域技能迁移及对动态视觉干扰项的鲁棒性方面均取得了更优性能，同时保持了适合现实世界部署的轻量级且具表达力的设计。
英文摘要
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.
