Uni-Skill：构建通用机器人操作的自演化技能库
Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
AI总结：
Uni-Skill通过自演化技能库提升机器人操作的通用性与适应性，实现高效技能检索与少样本推理。
摘要：
虽然基于技能的方法利用基础模型来增强组合任务的泛化能力，但它们通常依赖于固定的技能库，限制了在没有人工干预的情况下适应新任务的能力。为此，我们提出了Uni-Skill，一个统一的基于技能的框架，支持技能感知的规划，并促进自动技能进化。与以往仅限于预定义技能的 方法不同，Uni-Skill在现有技能不足时请求新的技能实现，确保具有自增强技能库的适应性规划。为了支持规划模块请求的多样化技能的自动实现，我们构建了SkillFolder，一个受VerbNet启发的仓库，源自大规模无结构机器人视频。SkillFolder引入了一种分层技能分类法，能够在多个抽象层次上捕捉多样的技能描述。通过用大规模自动标注的演示填充该分类法，Uni-Skill将技能获取的范式从低效的手动标注转变为高效的离线结构检索。检索到的示例为行为模式提供语义监督，并为空间轨迹提供细粒度的参考，使在部署时无需演示即可实现少样本技能推理。在模拟和现实环境中的全面实验验证了Uni-Skill在现有基于VLM的基于技能方法上的最先进性能，突显了其先进的推理能力和在广泛新任务上的强大零样本泛化能力。
英文摘要：
While skill-centric approaches leverage foundation models to enhance generalization in compositional tasks, they often rely on fixed skill libraries, limiting adaptability to new tasks without manual intervention. To address this, we propose Uni-Skill, a Unified Skill-centric framework that supports skill-aware planning and facilitates automatic skill evolution. Unlike prior methods that restrict planning to predefined skills, Uni-Skill requests for new skill implementations when existing ones are insufficient, ensuring adaptable planning with self-augmented skill library. To support automatic implementation of diverse skills requested by the planning module, we construct SkillFolder, a VerbNet-inspired repository derived from large-scale unstructured robotic videos. SkillFolder introduces a hierarchical skill taxonomy that captures diverse skill descriptions at multiple levels of abstraction. By populating this taxonomy with large-scale, automatically annotated demonstrations, Uni-Skill shifts the paradigm of skill acquisition from inefficient manual annotation to efficient offline structural retrieval. Retrieved examples provide semantic supervision over behavior patterns and fine-grained references for spatial trajectories, enabling few-shot skill inference without deployment-time demonstrations. Comprehensive experiments in both simulation and real-world settings verify the state-of-the-art performance of Uni-Skill over existing VLM-based skill-centric approaches, highlighting its advanced reasoning capabilities and strong zero-shot generalization across a wide range of novel tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
