Multi-step manipulation in dynamic environments remains challenging. Imitation learning (IL) is reactive but lacks compositional generalization, since monolithic policies do not decide which skill to reuse when scenes change. Classical task-and-motion planning (TAMP) offers compositionality, but its high planning latency prevents real-time failure recovery. We introduce SymSkill, a unified framework that jointly learns predicates, operators, and skills from unlabeled, unsegmented demonstrations, combining compositional generalization with real-time recovery. Offline, SymSkill learns symbolic abstractions and goal-oriented skills directly from demonstrations. Online, given a conjunction of learned predicates, it uses a symbolic planner to compose and reorder skills to achieve symbolic goals while recovering from failures at both the motion and symbolic levels in real time. Coupled with a compliant controller, SymSkill supports safe execution under human and environmental disturbances. In RoboCasa simulation, SymSkill executes 12 single-step tasks with 85% success and composes them into multi-step plans without additional data. On a real Franka robot, it learns from 5 minutes of play data and performs 12-step tasks from goal specifications. Code and additional analysis are available at https://symskill.github.io/ .

翻译：动态环境中的多步操控仍具挑战。模仿学习虽具反应性，但缺乏组合泛化能力——单体策略无法在场景变化时决定复用何种技能。经典任务与运动规划虽提供组合性，但其高规划延迟阻碍了实时故障恢复。我们提出SymSkill，一个统一框架，能够从未标注、未分割的示范中联合学习谓词、操作符与技能，兼顾组合泛化与实时恢复。离线阶段，SymSkill直接从示范中学习符号抽象与目标导向技能；在线阶段，给定已习得谓词的合取式，它利用符号规划器组合并重排技能以实现符号目标，同时在运动和符号层面实时恢复故障。结合合规控制器，SymSkill支持在人机交互与环境扰动下的安全执行。在RoboCasa仿真中，SymSkill以85%的成功率执行12个单步任务，并无需额外数据即可将其组合为多步规划。在真实Franka机器人上，它仅需5分钟示教数据即可从目标规范出发执行12步任务。代码与更多分析请见https://symskill.github.io/。