论文标题
技能-IL：在多任务模仿学习中解开技能和知识
SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning
论文作者
论文摘要
在这项工作中，我们介绍了一种新的观点，用于在多任务模仿学习中学习可转移的内容。人类能够转移技能和知识。如果我们可以循环工作并开车去商店，我们还可以骑自行车去商店并开车去上班。我们从中汲取灵感，假设策略网络的潜在记忆可以分为两个分区。这些要么包含有关任务的环境环境的知识，要么包含解决任务所需的可推广技能。这可以提高培训效率，并更好地概括相同环境中的技能组合以及在看不见的环境中的同一任务。 我们使用了建议的方法来训练两个不同的多任务IL环境的分离代理。在这两种情况下，我们的任务成功率都超过了SOTA的30％。我们还向真正的机器人进行了导航证明了这一点。
In this work, we introduce a new perspective for learning transferable content in multi-task imitation learning. Humans are able to transfer skills and knowledge. If we can cycle to work and drive to the store, we can also cycle to the store and drive to work. We take inspiration from this and hypothesize the latent memory of a policy network can be disentangled into two partitions. These contain either the knowledge of the environmental context for the task or the generalizable skill needed to solve the task. This allows improved training efficiency and better generalization over previously unseen combinations of skills in the same environment, and the same task in unseen environments. We used the proposed approach to train a disentangled agent for two different multi-task IL environments. In both cases we out-performed the SOTA by 30% in task success rate. We also demonstrated this for navigation on a real robot.
