Virtual Reality (VR) and Augmented Reality (AR) tools have been applied in
all engineering fields in order to avoid the use of physical prototypes, to
train in high-risk situations, and to interpret real or simulated results. In
order to complete a shared task or assign tasks to the agents in such immersive
environments, collaboration or Shared Cooperative Activities are a necessity.
Collaboration in immersive environments is an emerging field of research that
aims to study and enhance the ways in which people interact and work together
in Virtual and Augmented Reality settings. Collaboration in immersive
environments is a complex process that involves different factors such as
communication, coordination, and social presence. This paper provides an
overview of the current state of research on collaboration in immersive
environments. It discusses the different types of immersive environments,
including VR and AR, and the different forms of collaboration that can occur in
these environments. The paper also highlights the challenges and limitations of
collaboration in immersive environments, such as the lack of physical cues,
cost and usability and the need for further research in this area. Overall,
collaboration in immersive environments is a promising field with a wide range
of potential applications, from education to industry, and it can benefit both
individuals and groups by enhancing their ability to work together effectively.

虚拟现实（VR）和增强现实（AR）工具已应用于所有工程领域，以避免使用物理原型，高风险情况下的培训以及对真实或模拟结果的解释。这篇论文提供了关于沉浸环境中协作研究的现状概述，讨论了不同类型的沉浸环境以及可能发生在这些环境中的协作形式。论文还强调了沉浸环境中协作的挑战和局限性，如缺乏物理线索、成本和可用性以及需要进一步的研究。总体而言，沉浸环境中的协作是一个具有广泛应用潜力的有前景的领域，从教育到工业都能增强个体和团体的有效协作能力。

沉浸式环境中的协作：挑战与解决方案

Collaboration in Immersive Environments: Challenges and Solutions

We present an approach to sensorimotor control in immersive environments. Our
approach utilizes a high-dimensional sensory stream and a lower-dimensional
measurement stream. The cotemporal structure of these streams provides a rich
supervisory signal, which enables training a sensorimotor control model by
interacting with the environment. The model is trained using supervised
learning techniques, but without extraneous supervision. It learns to act based
on raw sensory input from a complex three-dimensional environment. The
presented formulation enables learning without a fixed goal at training time,
and pursuing dynamically changing goals at test time. We conduct extensive
experiments in three-dimensional simulations based on the classical
first-person game Doom. The results demonstrate that the presented approach
outperforms sophisticated prior formulations, particularly on challenging
tasks. The results also show that trained models successfully generalize across
environments and goals. A model trained using the presented approach won the
Full Deathmatch track of the Visual Doom AI Competition, which was held in
previously unseen environments.

在三维模拟环境中，提出了一种传感器运动控制的方法，它通过高维和低维度测量流来提供丰富的监管信号进行训练，并利用协同时间结构进行监督学习，无需外部监管即可学习三维环境的原始感官输入，其结果显示其学习效果优于先前复杂的公式，尤其适用于具有挑战性的任务。