One of the biggest hurdles robotics faces is the facet of sophisticated and
hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a
framework to address this problem. In parallel, the misgivings of robotics
offer a solid testing ground and evaluation metric for advancements in
reinforcement learning. The two disciplines go hand-in-hand, much like the
fields of Mathematics and Physics. By means of this survey paper, we aim to
invigorate links between the research communities of the two disciplines by
focusing on the work done in reinforcement learning for locomotive and control
aspects of robotics. Additionally, we aim to highlight not only the notable
successes but also the key challenges of the application of Reinforcement
Learning in Robotics. This paper aims to serve as a reference guide for
researchers in reinforcement learning applied to the field of robotics. The
literature survey is at a fairly introductory level, aimed at aspiring
researchers. Appropriately, we have covered the most essential concepts
required for research in the field of reinforcement learning, with robotics in
mind. Through a thorough analysis of this problem, we are able to manifest how
reinforcement learning could be applied profitably, and also focus on
open-ended questions, as well as the potential for future research.

本文旨在介绍强化学习在机器人自主行动和控制方面的应用，同时关注问题的关键挑战和未来研究方向。

机器人强化学习简介

A Concise Introduction to Reinforcement Learning in Robotics

Current summarization systems yield generic summaries that are disconnected
from users' preferences and expectations. To address this limitation, we
present CTRLsum, a novel framework for controllable summarization. Our approach
enables users to control multiple aspects of generated summaries by interacting
with the summarization system through textual input in the form of a set of
keywords or descriptive prompts. Using a single unified model, CTRLsum is able
to achieve a broad scope of summary manipulation at inference time without
requiring additional human annotations or pre-defining a set of control aspects
during training. We quantitatively demonstrate the effectiveness of our
approach on three domains of summarization datasets and five control aspects:
1) entity-centric and 2) length-controllable summarization, 3) contribution
summarization on scientific papers, 4) invention purpose summarization on
patent filings, and 5) question-guided summarization on news articles in a
reading comprehension setting. Moreover, when used in a standard, uncontrolled
summarization setting, CTRLsum achieves state-of-the-art results on the
CNN/DailyMail dataset. Code and model checkpoints are available at
this https URL

CTRLsum 是一种用于可控摘要的新型框架，它能够使用户通过文本输入关键词或描述性提示来控制生成摘要的多个方面，在不需要额外人工注释或预定义训练期间的控制方面的情况下，在三个领域的摘要数据集和五种控制方面中得到量化证明，并在 CNN/DailyMail 数据集上取得了最先进的结果。