论文标题
SIM2REAL用于用眼镜的钉孔插入
Sim2Real for Peg-Hole Insertion with Eye-in-Hand Camera
论文作者
论文摘要
即使钉孔插入是机器人技术中有充分研究的问题之一，但对于机器人来说仍然是一个挑战，尤其是在灵活性和推广能力方面。成功完成任务需要结合几种方式来应对现实世界的复杂性。在我们的工作中，我们专注于问题的视觉方面，并采用在模拟器中学习插入任务的策略。我们使用深度强化学习来学习终极的政策，然后将学习的模型转移到真正的机器人，而无需进行任何其他微调。我们表明，只采用RGB-D和联合信息（本体感受）的转移策略可以在实际机器人上表现良好。
Even though the peg-hole insertion is one of the well-studied problems in robotics, it still remains a challenge for robots, especially when it comes to flexibility and the ability to generalize. Successful completion of the task requires combining several modalities to cope with the complexity of the real world. In our work, we focus on the visual aspect of the problem and employ the strategy of learning an insertion task in a simulator. We use Deep Reinforcement Learning to learn the policy end-to-end and then transfer the learned model to the real robot, without any additional fine-tuning. We show that the transferred policy, which only takes RGB-D and joint information (proprioception) can perform well on the real robot.
