Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity. As a result, there exists an emerging research interest in the problem of achieving high operation accuracy while enhancing adaptability and compliance. However, previous works relied on an inefficient algorithm framework that separates motion planning from compliant control. Additionally, the compliant controller lacks robustness due to manually adjusted parameters. This paper proposes a novel Learning-based Adaptive Compliance algorithm (LAC) that improves the efficiency and robustness of symmetric bi-manual manipulation. Specifically, first, the algorithm framework combines desired trajectory generation with impedance-parameter adjustment to improve efficiency and robustness. Second, we introduce a centralized Actor-Critic framework with LSTM networks, enhancing the synchronization of bi-manual manipulation. LSTM networks pre-process the force states obtained by the agents, further ameliorating the performance of compliance operations. When evaluated in the dual-arm cooperative handling and peg-in-hole assembly experiments, our method outperforms baseline algorithms in terms of optimality and robustness.

翻译：对称双臂操作因其强大的负载能力，在各种在轨操作中至关重要。因此，如何在增强适应性和柔顺性的同时实现高操作精度，成为一个新兴的研究热点。然而，以往的研究依赖于一种将运动规划与柔顺控制分离的低效算法框架。此外，由于参数需手动调整，柔顺控制器的鲁棒性不足。本文提出一种新颖的基于学习的自适应柔顺算法（LAC），该算法提高了对称双臂操作的效率与鲁棒性。具体而言，首先，该算法框架将期望轨迹生成与阻抗参数调整相结合，以提升效率与鲁棒性。其次，我们引入了一种带有LSTM网络的集中式Actor-Critic框架，增强了双臂操作的同步性。LSTM网络对智能体获取的力状态进行预处理，进一步改善了柔顺操作的性能。在双臂协同搬运与轴孔装配实验中的评估表明，我们的方法在最优性和鲁棒性方面均优于基线算法。