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.

提出了一种 LAC 算法，该算法基于 Actor-Critic 框架和 LSTM 神经网络，通过结合期望轨迹生成和阻抗参数调整来提高协同操作效率和鲁棒性，其中 LSTM 网络对由代理人获得的力状态进行预处理，从而进一步改善了操作的稳定性和性能。实验结果表明，在协作处理和钉孔装配实验方面，我们的方法在最优性和鲁棒性方面优于基线算法。