A reinforcement learning (RL) based method that enables the robot to accomplish the assembly-type task with safety regulations is proposed. The overall strategy consists of grasping and assembly, and this paper mainly considers the assembly strategy. Force feedback is used instead of visual feedback to perceive the shape and direction of the hole in this paper. Furthermore, since the emergency stop is triggered when the force output is too large, a force-based dynamic safety lock (DSL) is proposed to limit the pressing force of the robot. Finally, we train and test the robot model with a simulator and build ablation experiments to illustrate the effectiveness of our method. The models are independently tested 500 times in the simulator, and we get an 88.57% success rate with a 4mm gap. These models are transferred to the real world and deployed on a real robot. We conducted independent tests and obtained a 79.63% success rate with a 4mm gap. Simulation environments: https://github.com/0707yiliu/peg-in-hole-with-RL.
翻译：推荐了一种基于强化学习(RL)的方法,使机器人能够用安全条例完成组装类型的任务。 总体策略包括抓取和组装, 本文主要考虑组装策略。 使用武力反馈, 而不是视觉反馈, 以观察本文中洞洞的形状和方向。 此外, 由于紧急停止是当力量输出太大时触发的, 提议了一种基于武力的动态安全锁( DSL) 来限制机器人的紧迫力量。 最后, 我们用模拟器来培训和测试机器人模型, 并用模拟器来建立模拟实验, 以说明我们的方法的有效性。 这些模型在模拟器中独立测试了500次, 我们获得了88.57%的成功率, 并有一个4毫米的空隙。 这些模型被转移到真实世界, 并被安装在真正的机器人上。 我们进行了独立测试, 获得了79.63%的成功率, 有4毫米的空隙。 模拟环境 : https://github.com/ 0707yiliu/peg- in- hole- RL 。</s>
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