基于先验知识：视觉-语言引导的神经符号模仿学习用于数据高效的现实机器人操作
Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation
AI总结：
本文提出一种可扩展的神经符号框架，通过少量未标注的示范数据自动构建符号规划领域和高效控制策略，无需手动领域工程，提升现实机器人操作的效率和可解释性。
摘要：
使机器人从少量示范中学习长周期操作任务仍然是机器人学中的核心挑战。现有神经符号方法通常依赖手工构建的符号抽象、语义标注轨迹或大规模示范数据集，限制了其可扩展性和现实应用。我们提出了一种可扩展的神经符号框架，能够从最少一个到三十个未标注的技能示范中自主构建符号规划领域和数据高效的控制策略，无需手动领域工程。我们的方法将示范分割为技能，并利用视觉-语言模型（VLM）来分类技能并识别等价的高层状态，从而实现自动构建状态转移图。该图通过答案集编程求解器处理，合成PDDL规划领域，该领域通过一个Oracle函数利用，以隔离每个技能策略的最小、任务相关和目标相对的观测和动作空间。策略在控制参考层面而非原始执行器信号层面学习，从而产生更平滑且噪声更少的学习目标。已知控制器可以通过将单个示范投影到场景中的其他物体上，用于现实数据增强，同时丰富图构建过程和模仿学习的数据集。我们主要在统计严谨的 manipulation 试验中验证了我们的框架在真实工业叉车上的有效性，并在两个标准基准上展示了跨平台的通用性。我们的结果表明，将控制学习、VLM驱动的抽象和自动规划合成整合到统一的流程中，构成了实现可扩展、数据高效、无专家和可解释的神经符号机器人学的实用路径。
英文摘要：
Enabling robots to learn long-horizon manipulation tasks from a handful of demonstrations remains a central challenge in robotics. Existing neuro-symbolic approaches often rely on hand-crafted symbolic abstractions, semantically labeled trajectories or large demonstration datasets, limiting their scalability and real-world applicability. We present a scalable neuro-symbolic framework that autonomously constructs symbolic planning domains and data-efficient control policies from as few as one to thirty unannotated skill demonstrations, without requiring manual domain engineering. Our method segments demonstrations into skills and employs a Vision-Language Model (VLM) to classify skills and identify equivalent high-level states, enabling automatic construction of a state-transition graph. This graph is processed by an Answer Set Programming solver to synthesize a PDDL planning domain, which an oracle function exploits to isolate the minimal, task-relevant and target relative observation and action spaces for each skill policy. Policies are learned at the control reference level rather than at the raw actuator signal level, yielding a smoother and less noisy learning target. Known controllers can be leveraged for real-world data augmentation by projecting a single demonstration onto other objects in the scene, simultaneously enriching the graph construction process and the dataset for imitation learning. We validate our framework primarily on a real industrial forklift across statistically rigorous manipulation trials, and demonstrate cross-platform generality on a Kinova Gen3 robotic arm across two standard benchmarks. Our results show that grounding control learning, VLM-driven abstraction, and automated planning synthesis into a unified pipeline constitutes a practical path toward scalable, data-efficient, expert-free and interpretable neuro-symbolic robotics.
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