With the increasing availability and affordability of personal robots, they
will no longer be confined to large corporate warehouses or factories but will
instead be expected to operate in less controlled environments alongside larger
groups of people. In addition to ensuring safety and efficiency, it is crucial
to minimize any negative psychological impact robots may have on humans and
follow unwritten social norms in these situations. Our research aims to develop
a model that can predict the movements of pedestrians and perceptually-social
groups in crowded environments. We introduce a new Social Group Long Short-term
Memory (SG-LSTM) model that models human groups and interactions in dense
environments using a socially-aware LSTM to produce more accurate trajectory
predictions. Our approach enables navigation algorithms to calculate
collision-free paths faster and more accurately in crowded environments.
Additionally, we also release a large video dataset with labeled pedestrian
groups for the broader social navigation community. We show comparisons with
different metrics on different datasets (ETH, Hotel, MOT15) and different
prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime
performance.

本研究开发了一个模型用于预测人群和人群之间的交互，在拥挤的环境下使用 SG-LSTM 模型以更准确地预测轨迹，同时提供一个标记了行人组的大型视频数据集。

SG-LSTM: 社交群组 LSTM​ 用于机器人在密集人群中的导航

SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds

It is fundamental for personal robots to reliably navigate to a specified
goal. To study this task, PointGoal navigation has been introduced in simulated
Embodied AI environments. Recent advances solve this PointGoal navigation task
with near-perfect accuracy (99.6% success) in photo-realistically simulated
environments, assuming noiseless egocentric vision, noiseless actuation, and
most importantly, perfect localization. However, under realistic noise models
for visual sensors and actuation, and without access to a "GPS and Compass
sensor," the 99.6%-success agents for PointGoal navigation only succeed with
0.3%. In this work, we demonstrate the surprising effectiveness of visual
odometry for the task of PointGoal navigation in this realistic setting, i.e.,
with realistic noise models for perception and actuation and without access to
GPS and Compass sensors. We show that integrating visual odometry techniques
into navigation policies improves the state-of-the-art on the popular Habitat
PointNav benchmark by a large margin, improving success from 64.5% to 71.7%
while executing 6.4 times faster.

本研究使用实际噪声模型和视觉里程法，改进了 Habitat PointNav 基准中 PointGoal 导航任务的成功率，成功率从 64.5% 提高到 71.7%，速度提高了 6.4 倍。

视觉测距技术在点目标导航中的出人意料有效性

The Surprising Effectiveness of Visual Odometry Techniques for Embodied  PointGoal Navigation

Personal robots and driverless cars need to be able to operate in novel
environments and thus quickly and efficiently learn to recognise new object
classes. We address this problem by considering the task of video object
segmentation. Previous accurate methods for this task finetune a model using
the first annotated frame, and/or use additional inputs such as optical flow
and complex post-processing. In contrast, we develop a fast, causal algorithm
that requires no finetuning, auxiliary inputs or post-processing, and segments
a variable number of objects in a single forward-pass. We represent an object
with clusters, or "visual words", in the embedding space, which correspond to
object parts in the image space. This allows us to robustly match to the
reference objects throughout the video, because although the global appearance
of an object changes as it undergoes occlusions and deformations, the
appearance of more local parts may stay consistent. We learn these visual words
in an unsupervised manner, using meta-learning to ensure that our training
objective matches our inference procedure. We achieve comparable accuracy to
finetuning based methods (whilst being 1 to 2 orders of magnitude faster), and
state-of-the-art in terms of speed/accuracy trade-offs on four video
segmentation datasets. Code is available at
this https URL

通过使用聚类，元学习和嵌入式空间中的视觉单词等技术，我们开发了一种快速，因果算法，可在单个前向传递中分割可变数量的对象，并在四个视频分割数据集上实现了最新的速度 / 精度折衷，在不需要调优，附加输入或后处理的情况下。

基于元学习的深度视觉词语快速视频对象分割

Meta Learning Deep Visual Words for Fast Video Object Segmentation

Personal robots are expected to interact with the user by recognizing the
user's face. However, in most of the service robot applications, the user needs
to move himself/herself to allow the robot to see him/her face to face. To
overcome such limitations, a method for estimating human body orientation is
required. Previous studies used various components such as feature extractors
and classification models to classify the orientation which resulted in low
performance. For a more robust and accurate approach, we propose the light
weight convolutional neural networks, an end to end system, for estimating
human body orientation. Our body orientation estimation model achieved 81.58%
and 94% accuracy with the benchmark dataset and our own dataset respectively.
The proposed method can be used in a wide range of service robot applications
which depend on the ability to estimate human body orientation. To show its
usefulness in service robot applications, we designed a simple robot
application which allows the robot to move towards the user's frontal plane.
With this, we demonstrated an improved face detection rate.

提出一种基于轻量级卷积神经网络的人体方向估计方法，用于改善面向服务型机器人应用中用户与机器人面对面交互时的限制，该方法在基准数据集和自行构建的数据集中分别达到了 81.58% 和 94% 的准确率，并可用于多种对估计人体方向敏感的面向服务型机器人应用，同时欲演示该方法对面部检测率的提升。