Long-horizon manipulation tasks such as stacking represent a longstanding challenge in the field of robotic manipulation, particularly when using reinforcement learning (RL) methods which often struggle to learn the correct sequence of actions for achieving these complex goals. To learn this sequence, symbolic planning methods offer a good solution based on high-level reasoning, however, planners often fall short in addressing the low-level control specificity needed for precise execution. This paper introduces a novel framework that integrates symbolic planning with hierarchical RL through the cooperation of high-level operators and low-level policies. Our contribution integrates planning operators (e.g. preconditions and effects) as part of the hierarchical RL algorithm based on the Scheduled Auxiliary Control (SAC-X) method. We developed a dual-purpose high-level operator, which can be used both in holistic planning and as independent, reusable policies. Our approach offers a flexible solution for long-horizon tasks, e.g., stacking a cube. The experimental results show that our proposed method obtained an average of 97.2% success rate for learning and executing the whole stack sequence, and the success rate for learning independent policies, e.g. reach (98.9%), lift (99.7%), stack (85%), etc. The training time is also reduced by 68% when using our proposed approach.

翻译：长视野操作任务（如叠放）是机器人操作领域长期存在的挑战，尤其在使用强化学习方法时，这些方法通常难以学会实现复杂目标的正确动作序列。为学习该序列，符号规划方法基于高层推理提供了良好解决方案，但规划器往往无法满足精确执行所需的低层控制特异性。本文提出一种新颖框架，通过高层操作符与低层策略的协同，将符号规划与分层强化学习相结合。我们的贡献在于将规划操作符（如前提条件和效果）整合进基于调度辅助控制（SAC-X）方法的分层强化学习算法中。我们开发了一种双用途的高层操作符，既可用于整体规划，也可作为独立可复用的策略。本方法为长视野任务（如立方体叠放）提供了灵活解决方案。实验结果表明，所提方法在学习并执行完整叠放序列时平均成功率达97.2%，独立策略（如抓取（98.9%）、抬升（99.7%）、堆叠（85%）等）的学习成功率亦有显著提升。采用本方法后，训练时间减少了68%。