论文标题
Glocal：多个任务的glocalized课程辅助学习，并应用于机器人抓握
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping
论文作者
论文摘要
机器人技术的领域很具有挑战性，因为需要大量数据并确保学习过程中的安全性，因此需要进行深入的强化学习。课程学习在样本有效的深度学习方面表现出良好的表现。在本文中，我们提出了一种算法（名为GloCal），该算法根据其评估分数为群集任务创建了一个代理商学习多个离散任务的课程。从表现最高的集群中，确定了集群的全球任务代表，以学习将转移到随后形成的新集群转移的全球策略，而集群中的其余任务则是本地策略。比较了我们的glocal算法的功效和效率与49个具有多样的物体复杂性和掌握难度的对象的掌握域中的其他方法进行了比较！数据集。结果表明，Glocal能够学会掌握100％的物体，而其他方法则达到了最多86％，尽管训练时间更长1.5倍。
The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning has shown good performance in terms of sample- efficient deep learning. In this paper, we propose an algorithm (named GloCAL) that creates a curriculum for an agent to learn multiple discrete tasks, based on clustering tasks according to their evaluation scores. From the highest-performing cluster, a global task representative of the cluster is identified for learning a global policy that transfers to subsequently formed new clusters, while the remaining tasks in the cluster are learned as local policies. The efficacy and efficiency of our GloCAL algorithm are compared with other approaches in the domain of grasp learning for 49 objects with varied object complexity and grasp difficulty from the EGAD! dataset. The results show that GloCAL is able to learn to grasp 100% of the objects, whereas other approaches achieve at most 86% despite being given 1.5 times longer training time.
