Collaborative robots or cobots interact with humans in a common work
environment. In cobots, one under investigated but important issue is related
to their movement and how it is perceived by humans. This paper tries to
analyze whether humans prefer a robot moving in a human or in a robotic
fashion. To this end, the present work lays out what differentiates the
movement performed by an industrial robotic arm from that performed by a human
one. The main difference lies in the fact that the robotic movement has a
trapezoidal speed profile, while for the human arm, the speed profile is
bell-shaped and during complex movements, it can be considered as a sum of
superimposed bell-shaped movements. Based on the lognormality principle, a
procedure was developed for a robotic arm to perform human-like movements. Both
speed profiles were implemented in two industrial robots, namely, an ABB IRB
120 and a Universal Robot UR3. Three tests were used to study the subjects'
preference when seeing both movements and another analyzed the same when
interacting with the robot by touching its ends with their fingers.

研究分析人们对协作机器人的运动方式的偏好，探讨工业机器人的运动与人体运动之间的主要差异，并基于对数正态原理开发了模拟人类运动的程序。通过实施不同的速度轮廓在两个工业机器人中进行测试，以研究人们对不同运动方式的偏好及与机器人互动时的感知。

机器人中的均匀与对数正态运动：对机器人运动的感知偏好

Uniform vs. Lognormal Kinematics in Robots: Perceptual Preferences for  Robotic Movements

With the emergence of collaborative robots (cobots), human-robot
collaboration in industrial manufacturing is coming into focus. For a cobot to
act autonomously and as an assistant, it must understand human actions during
assembly. To effectively train models for this task, a dataset containing
suitable assembly actions in a realistic setting is crucial. For this purpose,
we present the ATTACH dataset, which contains 51.6 hours of assembly with 95.2k
annotated fine-grained actions monitored by three cameras, which represent
potential viewpoints of a cobot. Since in an assembly context workers tend to
perform different actions simultaneously with their two hands, we annotated the
performed actions for each hand separately. Therefore, in the ATTACH dataset,
more than 68% of annotations overlap with other annotations, which is many
times more than in related datasets, typically featuring more simplistic
assembly tasks. For better generalization with respect to the background of the
working area, we did not only record color and depth images, but also used the
Azure Kinect body tracking SDK for estimating 3D skeletons of the worker. To
create a first baseline, we report the performance of state-of-the-art methods
for action recognition as well as action detection on video and
skeleton-sequence inputs. The dataset is available at
this https URL .

以协作型机器人为代表的人机协作正在工业制造业中成为研究热点，为了提高其自主性与辅助功能，我们设计了 ATTACH 数据集，该数据集包含超过 51 小时的装配活动，涵盖了 95.2k 个精细注释的动作，为研发基于三维视觉的自主装配机器人提供了支持。