Articulated object manipulation is a challenging task, requiring constrained motion and adaptive control to handle the unknown dynamics of the manipulated objects. While reinforcement learning (RL) has been widely employed to tackle various scenarios and types of articulated objects, the complexity of these tasks, stemming from multiple intertwined objectives makes learning a control policy in the full task space highly difficult. To address this issue, we propose a Subspace-wise hybrid RL (SwRL) framework that learns policies for each divided task space, or subspace, based on independent objectives. This approach enables adaptive force modulation to accommodate the unknown dynamics of objects. Additionally, it effectively leverages the previously underlooked redundant subspace, thereby maximizing the robot's dexterity. Our method enhances both learning efficiency and task execution performance, as validated through simulations and real-world experiments. Supplementary video is available at https://youtu.be/PkNxv0P8Atk

翻译：铰接物体操作是一项具有挑战性的任务，需要约束运动和自适应控制以处理被操作物体的未知动力学特性。虽然强化学习已被广泛应用于处理各种场景和类型的铰接物体，但由于多个相互交织的目标导致任务复杂性高，在全任务空间中学习控制策略极为困难。为解决这一问题，我们提出了一种基于子空间的混合强化学习框架，该框架根据独立目标为每个划分的任务空间（即子空间）学习策略。该方法能够实现自适应力调节以适应物体的未知动力学特性。此外，它有效利用了先前被忽视的冗余子空间，从而最大化机器人的灵巧性。通过仿真和真实世界实验验证，我们的方法在提升学习效率和任务执行性能方面均表现出色。补充视频可在 https://youtu.be/PkNxv0P8Atk 查看。