分空间混合强化学习，让机械臂更灵活地操作可动物体
Subspace-wise Hybrid RL for Articulated Object Manipulation
- 将任务分解到不同子空间，分别学习控制策略
- 实验证明学习效率与操作性能显著提升
- 适合需要精细力控的机器人操作场景
可动物体操作是一项挑战性任务，需在运动约束下适应未知动力学。尽管强化学习广泛应用于各类可动物体操作，但多目标交织导致全任务空间学习困难。为此，我们提出分空间混合强化学习（SwRL）框架，基于独立目标为每个划分的任务子空间学习策略，实现自适应力调控以应对未知物体动力学，并有效利用此前被忽视的冗余子空间，最大化机器人灵巧性。通过仿真与真实实验验证，该方法提升了学习效率与任务执行性能。
原文摘要 · Abstract (English)
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
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