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.

本文提出了一种名为 GloCAL 的算法，通过对多个离散任务进行聚类，创建课程学习，可以有效且安全地在机器人领域应用深度强化学习，并且利用所提出的算法，在 49 个对象的抓取学习中可以得到比其他方法更好的效果。

GloCAL：基于本地化课程辅助学习的多任务学习及其在机器人抓取中的应用

GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping

Grasp detection in clutter requires the robot to reason about the 3D scene
from incomplete and noisy perception. In this work, we draw insight that 3D
reconstruction and grasp learning are two intimately connected tasks, both of
which require a fine-grained understanding of local geometry details. We thus
propose to utilize the synergies between grasp affordance and 3D reconstruction
through multi-task learning of a shared representation. Our model takes
advantage of deep implicit functions, a continuous and memory-efficient
representation, to enable differentiable training of both tasks. We train the
model on self-supervised grasp trials data in simulation. Evaluation is
conducted on a clutter removal task, where the robot clears cluttered objects
by grasping them one at a time. The experimental results in simulation and on
the real robot have demonstrated that the use of implicit neural
representations and joint learning of grasp affordance and 3D reconstruction
have led to state-of-the-art grasping results. Our method outperforms baselines
by over 10% in terms of grasp success rate. Additional results and videos can
be found at this https URL

利用深度隐式函数的多任务学习、共享表征，联合学习抓握可行性和三维重建任务，在清理杂物方面实现了优于基线方法 10% 的抓握成功率。