Multi-robot decision-making is the process where multiple robots coordinate
actions. In this paper, we aim for efficient and effective multi-robot
decision-making despite the robots' limited on-board resources and the often
resource-demanding complexity of their tasks. We introduce the first algorithm
enabling the robots to choose with which few other robots to coordinate and
provably balance the trade-off of centralized vs. decentralized coordination.
Particularly, centralization favors globally near-optimal decision-making but
at the cost of increased on-board resource requirements; whereas,
decentralization favors minimal resource requirements but at a global
suboptimality cost. All robots can thus afford our algorithm, irrespective of
their resources. We are motivated by the future of autonomy that involves
multiple robots coordinating actions to complete resource-demanding tasks, such
as target tracking, area coverage, and monitoring. To provide closed-form
guarantees, we focus on maximization problems involving monotone and "doubly"
submodular functions. To capture the cost of decentralization, we introduce the
notion of Centralization Of Information among non-Neighbors (COIN). We validate
our algorithm in simulated scenarios of image covering.

本研究介绍了第一个算法，使得多个机器人可以选择与少数其他机器人协调，并能够在中央集权和去中心化协调之间平衡权衡，从而实现高效有效的多机器人决策制定，而不必考虑机器人的资源限制或任务复杂度。为了提供封闭形式保证，本文关注于涵盖单调和 “双重” 子模型函数的权重最大化问题，并引入了非相邻信息的集中化（COIN）的概念，以捕捉去中心化成本。

资源感知的分布式次模型最大化：多机器人决策制定的范例

Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making

Thanks to the substantial and explosively inscreased instructional videos on
the Internet, novices are able to acquire knowledge for completing various
tasks. Over the past decade, growing efforts have been devoted to investigating
the problem on instructional video analysis. However, the most existing
datasets in this area have limitations in diversity and scale, which makes them
far from many real-world applications where more diverse activities occur. To
address this, we present a large-scale dataset named as "COIN" for
COmprehensive INstructional video analysis. Organized with a hierarchical
structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains
(e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed
toolbox, all the videos are annotated efficiently with a series of step labels
and the corresponding temporal boundaries. In order to provide a benchmark for
instructional video analysis, we evaluate plenty of approaches on the COIN
dataset under five different settings. Furthermore, we exploit two important
characteristics (i.e., task-consistency and ordering-dependency) for localizing
important steps in instructional videos. Accordingly, we propose two simple yet
effective methods, which can be easily plugged into conventional proposal-based
action detection models. We believe the introduction of the COIN dataset will
promote the future in-depth research on instructional video analysis for the
community. Our dataset, annotation toolbox and source code are available at
this http URL

这篇论文介绍了一个名为 'COIN' 的大规模视频数据集，包含了 180 个任务的 11,827 个视频，并提供了一种新的工具箱进行视频的标注和步骤划分，同时开发了两种效果良好的方法来定位重要的步骤，这将有助于进一步研究视频分析领域。