一句话：提出 RoboScribe，从原始连续演示中自动推导状态与动作抽象，迭代精化并合成可解释的机器人控制程序。
深度强化学习虽推动了机器人系统发展，但神经网络架构复杂，可信度难以保证。程序化强化学习为此提供了有希望的路径，然而现有机器人控制程序合成方法依赖含用户定义状态抽象谓词与低层控制器库的领域特定语言，在缺乏这些预定义组件的未知环境中并不实用。本文提出 RoboScribe，一种抽象精化引导的程序合成框架，能够从高维连续空间中原始、未分割的任务演示中自动导出机器人状态与动作抽象。该方法迭代地丰富并精化初始粗糙的抽象，直至在抽象后的机器人环境上生成可完成任务的程序；通过直接从机器人原始连续状态与动作空间中推断可复现子程序，有效合成迭代程序。实验表明，RoboScribe 生成的程序可归纳泛化到涉及任意数量物体的长时程机器人任务，并在可解释性与效率上优于基线方法。
Over the past decade, deep reinforcement learning (RL) techniques have significantly advanced robotic systems. However, due to the complex architectures of neural network models, ensuring their trustworthiness is a considerable challenge. Programmatic reinforcement learning has surfaced as a promising approach. Nonetheless, synthesizing robot-control programs remains challenging. Existing methods rely on domain-specific languages (DSLs) populated with user-defined state abstraction predicates and a library of low-level controllers as abstract actions to boot synthesis, which is impractical in unknown environments that lack such predefined components. To address this limitation, we introduce RoboScribe, a novel abstraction refinement-guided program synthesis framework that automatically derives robot state and action abstractions from raw, unsegmented task demonstrations in high-dimensional, continuous spaces. It iteratively enriches and refines an initially coarse abstraction until it generates a task-solving program over the abstracted robot environment. RoboScribe is effective in synthesizing iterative programs by inferring recurring subroutines directly from the robot’s raw, continuous state and action spaces, without needing predefined abstractions. Experimental results show that RoboScribe programs inductively generalize to long-horizon robot tasks involving arbitrary numbers of objects, outperforming baseline methods in terms of both interpretability and efficiency.
@article{paperbot3024,
title = {Abstraction Refinement-Guided Program Synthesis for Robot Learning from Demonstrations},
author = {Guofeng Cui and Yuning Wang and Wensen Mao and Yuanlin Duan and He Zhu},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {OOPSLA2},
year = {2025},
doi = {10.1145/3763070}
}