When transferring a Deep Reinforcement Learning model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world well in many circumstances. This results in a long period of fine-tuning in the real world. This paper proposes a self-supervised vision-based DRL method that allows robots to pick and place objects effectively and efficiently when directly transferring a training model from simulation to the real world. A height-sensitive action policy is specially designed for the proposed method to deal with crowded and stacked objects in challenging environments. The training model with the proposed approach can be applied directly to a real suction task without any fine-tuning from the real world while maintaining a high suction success rate. It is also validated that our model can be deployed to suction novel objects in a real experiment with a suction success rate of 90\% without any real-world fine-tuning. The experimental video is available at: https://youtu.be/jSTC-EGsoFA.

翻译：在将深度强化学习模型从仿真环境迁移至现实世界时，由于仿真在许多情况下无法完美模拟真实场景，模型性能可能不尽如人意，导致需要在现实环境中进行长时间的微调。本文提出一种基于自监督视觉的深度强化学习方法，使机器人能够在将训练模型直接从仿真迁移至现实世界时，高效且有效地完成物体拾取与放置任务。针对该方法，我们设计了一种高度敏感的动作策略，以应对拥挤堆叠物体等复杂环境。采用所提方法训练的模型可直接应用于真实吸盘任务，无需任何现实世界微调即可保持较高的吸盘成功率。实验进一步验证，该模型在零真实世界微调条件下，对未知物体的吸盘成功率可达90%。实验视频链接：https://youtu.be/jSTC-EGsoFA。