  Deep reinforcement learning has made significant progress in robotic
manipulation tasks and it works well in the ideal disturbance-free environment.
However, in a real-world environment, both internal and external disturbances
are inevitable, thus the performance of the trained policy will dramatically
drop. To improve the robustness of the policy, we introduce the adversarial
training mechanism to the robotic manipulation tasks in this paper, and an
adversarial skill learning algorithm based on soft actor-critic (SAC) is
proposed for robust manipulation. Extensive experiments are conducted to
demonstrate that the learned policy is robust to internal and external
disturbances. Additionally, the proposed algorithm is evaluated in both the
simulation environment and on the real robotic platform.
