The use of AI in public spaces continually raises concerns about privacy and
the protection of sensitive data. An example is the deployment of detection and
recognition methods on humans, where images are provided by surveillance
cameras. This results in the acquisition of great amounts of sensitive data,
since the capture and transmission of images taken by such cameras happens
unaltered, for them to be received by a server on the network. However, many
applications do not explicitly require the identity of a given person in a
scene; An anonymized representation containing information of the person's
position while preserving the context of them in the scene suffices. We show
how using a customized loss function on region of interests (ROI) can achieve
sufficient anonymization such that human faces become unrecognizable while
persons are kept detectable, by training an end-to-end optimized autoencoder
for learned image compression that utilizes the flexibility of the learned
analysis and reconstruction transforms for the task of mutating parts of the
compression result. This approach enables compression and anonymization in one
step on the capture device, instead of transmitting sensitive, nonanonymized
data over the network. Additionally, we evaluate how this anonymization impacts
the average precision of pre-trained foundation models on detecting faces
(MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while
considering compression rate and latency.

在公共空间使用人工智能不断引发对隐私和敏感数据保护的担忧，本论文介绍了一种使用定制损失函数的 ROI 来实现足够去识别化的方法，通过训练端到端优化的自编码器来实现压缩和去识别化，同时考虑了压缩率、延迟和对人脸和人物检测模型的影响。

用于匿名学习图像压缩的感兴趣区域损失

Region of Interest Loss for Anonymizing Learned Image Compression

The 'intuitive' trust people feel when encountering robots in public spaces
is a key determinant of their willingness to cooperate with these robots. We
conducted four experiments to study this topic in the context of peacekeeping
robots. Participants viewed scenarios, presented as static images or
animations, involving a robot or a human guard performing an access-control
task. The guards interacted more or less politely with younger and older male
and female people. Our results show strong effects of the guard's politeness.
Age and sex of the people interacting with the guard had no significant effect
on participants' impressions of its attributes. There were no differences
between responses to robot and human guards. This study advances the notion
that politeness is a crucial determinant of people's perception of peacekeeping
robots.

研究了在公共空间与机器人相遇时人们感到的直观信任是他们是否愿意与机器人合作的关键决定因素，并表明当互动机器人在保安角色下表现得非常有礼貌时，人们对其印象更好。

礼貌至上：维和机器人的感知

Politeness Counts: Perceptions of Peacekeeping Robots

Robot navigation in crowded public spaces is a complex task that requires
addressing a variety of engineering and human factors challenges. These
challenges have motivated a great amount of research resulting in important
developments for the fields of robotics and human-robot interaction over the
past three decades. Despite the significant progress and the massive recent
interest, we observe a number of significant remaining challenges that prohibit
the seamless deployment of autonomous robots in public pedestrian environments.
In this survey article, we organize existing challenges into a set of
categories related to broader open problems in motion planning, behavior
design, and evaluation methodologies. Within these categories, we review past
work, and offer directions for future research. Our work builds upon and
extends earlier survey efforts by a) taking a critical perspective and
diagnosing fundamental limitations of adopted practices in the field and b)
offering constructive feedback and ideas that we aspire will drive research in
the field over the coming decade.

本文介绍了机器人在拥挤的公共场所中导航的挑战，分类了现有的问题，并提供了未来研究的方向和建议。

社交机器人导航的核心挑战：一项调查研究

Core Challenges of Social Robot Navigation: A Survey

In the EU-funded MuMMER project, we have developed a social robot designed to
interact naturally and flexibly with users in public spaces such as a shopping
mall. We present the latest version of the robot system developed during the
project. This system encompasses audio-visual sensing, social signal
processing, conversational interaction, perspective taking, geometric
reasoning, and motion planning. It successfully combines all these components
in an overarching framework using the Robot Operating System (ROS) and has been
deployed to a shopping mall in Finland interacting with customers. In this
paper, we describe the system components, their interplay, and the resulting
robot behaviours and scenarios provided at the shopping mall.

本文介绍了在欧盟资助的 MuMMER 项目中开发的社交机器人，其系统包括音频 - 视觉感应、社交信号处理、对话式互动、透视思考、几何推理和运动规划，并将所有这些组件成功结合在 ROS 中，已部署到芬兰的购物中心与顾客进行交互的最新版本。