适应性和可转移的精细工业插入的容差引导策略学习
Tolerance-Guided Policy Learning for Adaptable and Transferrable Delicate Industrial Insertion
摘要：
策略学习，用于精细的工业插入任务（例如，印制电路板组装）是具有挑战性的。本文考虑两个主要问题：如何学习一个多样化的政策（而不是只有一个平均政策），可以有效地处理不同的工件与最小量的训练数据，以及如何处理插入过程中的工件缺陷。为了解决这些问题，我们提出了宽容引导的策略学习。为了鼓励学习到的策略到不同工件的可转移性，我们使用插入容差将任务嵌入添加到策略的输入空间。然后，我们训练的政策，使用生成对抗模仿学习与奖励塑造（RS-GAIL）的各种代表性的情况。为了鼓励学习策略的适应性，以处理缺陷，我们建立了一个概率推理模型，可以输出最佳的插入姿势的基础上失败的插入使用公差模型。然后，最佳插入姿势被用作对所学习的策略的参考。该方法在一系列IC插座插入任务的仿真中得到了验证。结果表明：1）RS-GAIL在稀疏回报条件下能够有效地学习最优策略; 2）容差嵌入增强了学习策略的可移植性; 3）概率推理使策略对工件缺陷具有鲁棒性。
英文摘要：
Policy learning for delicate industrial insertion tasks (e.g., PC board assembly) is challenging. This paper considers two major problems: how to learn a diversified policy (instead of just one average policy) that can efficiently handle different workpieces with minimum amount of training data, and how to handle defects of workpieces during insertion. To address the problems, we propose tolerance-guided policy learning. To encourage transferability of the learned policy to different workpieces, we add a task embedding to the policy's input space using the insertion tolerance. Then we train the policy using generative adversarial imitation learning with reward shaping (RS-GAIL) on a variety of representative situations. To encourage adaptability of the learned policy to handle defects, we build a probabilistic inference model that can output the best inserting pose based on failed insertions using the tolerance model. The best inserting pose is then used as a reference to the learned policy. This proposed method is validated on a sequence of IC socket insertion tasks in simulation. The results show that 1) RS-GAIL can efficiently learn optimal policies under sparse rewards; 2) the tolerance embedding can enhance the transferability of the learned policy; 3) the probabilistic inference makes the policy robust to defects on the workpieces.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
