论文标题
转移知识以在接触良好的操作中进行增强学习
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation
论文作者
论文摘要
在制造业中，由于不同环境的变化动态，组装任务一直是学习算法的挑战。增强学习（RL）是自动学习这些任务的有前途的框架，但是，即使部署条件仅略有不同，也不容易应用学习的策略或技能，即解决任务的能力，即使解决任务的能力。在本文中，我们通过利用多个技能先验来解决在类似任务的家庭中转移知识的挑战。我们建议通过比较目标任务和先前的任务之间的相似性来学习完成每个任务所需的特定技能的先前分布，并组成技能先验的家族。我们的方法学习了一个潜在的动作空间，该空间代表每个先前任务的轨迹中嵌入的技能。我们已经在一组插孔插入任务上评估了我们的方法，并证明了对训练期间从未遇到的新任务的更好概括。
In manufacturing, assembly tasks have been a challenge for learning algorithms due to variant dynamics of different environments. Reinforcement learning (RL) is a promising framework to automatically learn these tasks, yet it is still not easy to apply a learned policy or skill, that is the ability of solving a task, to a similar environment even if the deployment conditions are only slightly different. In this paper, we address the challenge of transferring knowledge within a family of similar tasks by leveraging multiple skill priors. We propose to learn prior distribution over the specific skill required to accomplish each task and compose the family of skill priors to guide learning the policy for a new task by comparing the similarity between the target task and the prior ones. Our method learns a latent action space representing the skill embedding from demonstrated trajectories for each prior task. We have evaluated our method on a set of peg-in-hole insertion tasks and demonstrate better generalization to new tasks that have never been encountered during training.
