The Reinforcement Learning (RL) paradigm has been an essential tool for
automating robotic tasks. Despite the advances in RL, it is still not widely
adopted in the industry due to the need for an expensive large amount of robot
interaction with its environment. Curriculum Learning (CL) has been proposed to
expedite learning. However, most research works have been only evaluated in
simulated environments, from video games to robotic toy tasks. This paper
presents a study for accelerating robot learning of contact-rich manipulation
tasks based on Curriculum Learning combined with Domain Randomization (DR). We
tackle complex industrial assembly tasks with position-controlled robots, such
as insertion tasks. We compare different curricula designs and sampling
approaches for DR. Based on this study, we propose a method that significantly
outperforms previous work, which uses DR only (No CL is used), with less than a
fifth of the training time (samples). Results also show that even when training
only in simulation with toy tasks, our method can learn policies that can be
transferred to the real-world robot. The learned policies achieved success
rates of up to 86\% on real-world complex industrial insertion tasks (with
tolerances of $\pm 0.01~mm$) not seen during the training.

本研究结合课程学习和域随机化方法，探究如何快速应用于机器人接触式操作任务中，实现在工业装配等领域中的快速学习和短时间内达到高成功率。研究结果表明，本文所提出的方法在工业插入任务中取得了最多 86% 的成功率，并且使用样本训练的时间不到以往的五分之一。

加速机器人学习接触丰富的操纵：一个课程学习研究

Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study

Task-relevant grasping is critical for industrial assembly, where downstream
manipulation tasks constrain the set of valid grasps. Learning how to perform
this task, however, is challenging, since task-relevant grasp labels are hard
to define and annotate. There is also yet no consensus on proper
representations for modeling or off-the-shelf tools for performing
task-relevant grasps. This work proposes a framework to learn task-relevant
grasping for industrial objects without the need of time-consuming real-world
data collection or manual annotation. To achieve this, the entire framework is
trained solely in simulation, including supervised training with synthetic
label generation and self-supervised, hand-object interaction. In the context
of this framework, this paper proposes a novel, object-centric canonical
representation at the category level, which allows establishing dense
correspondence across object instances and transferring task-relevant grasps to
novel instances. Extensive experiments on task-relevant grasping of
densely-cluttered industrial objects are conducted in both simulation and
real-world setups, demonstrating the effectiveness of the proposed framework.
Code and data are available at this https URL

通过模拟学习，本文提出了一种用于学习工业物体的任务相关抓取的框架，同时提出了一种新颖的分类层面的物体中心规范表示，可以在物体实例之间建立密集通信，并将任务相关抓取传递到新实例中。

从模拟中学习类别级别任务相关难点抓取技能

CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from  Simulation

Over the past several years there has been a considerable research investment
into learning-based approaches to industrial assembly, but despite significant
progress these techniques have yet to be adopted by industry. We argue that it
is the prohibitively large design space for Deep Reinforcement Learning (DRL),
rather than algorithmic limitations per se, that are truly responsible for this
lack of adoption. Pushing these techniques into the industrial mainstream
requires an industry-oriented paradigm which differs significantly from the
academic mindset. In this paper we define criteria for industry-oriented DRL,
and perform a thorough comparison according to these criteria of one family of
learning approaches, DRL from demonstration, against a professional industrial
integrator on the recently established NIST assembly benchmark. We explain the
design choices, representing several years of investigation, which enabled our
DRL system to consistently outperform the integrator baseline in terms of both
speed and reliability. Finally, we conclude with a competition between our DRL
system and a human on a challenge task of insertion into a randomly moving
target. This study suggests that DRL is capable of outperforming not only
established engineered approaches, but the human motor system as well, and that
there remains significant room for improvement. Videos can be found on our
project website: this https URL

通过与基于传统工程方法的专业工业集成器的比较，研究表明基于深度强化学习的工业装配方法不仅可以超越已有的工业装配方法，还能超越人类运动系统，并且数据说明还有巨大的优化空间。