The influence of individual differences on the perception and evaluation of interactions with robots has been researched for decades. Some human demographic characteristics have been shown to affect how individuals perceive interactions with robots. Still, it is to-date not clear whether, which and to what extent individual differences influence how we perceive robots, and even less is known about human factors and their effect on the perception of robot movements. In addition, most results on the relevance of individual differences investigate human-robot interactions with humanoid or social robots whereas interactions with industrial robots are underrepresented. We present a literature review on the relationship of robot movements and the influence of demographic variation. Our review reveals a limited comparability of existing findings due to a lack of standardized robot manipulations, various dependent variables used and differing experimental setups including different robot types. In addition, most studies have insufficient sample sizes to derive generalizable results. To overcome these shortcomings, we report the results from a Web-based experiment with 930 participants that studies the effect of demographic characteristics on the evaluation of movement behaviors of an articulated robot arm. Our findings demonstrate that most participants prefer an approach from the side, a large movement range, conventional numbers of rotations, smooth movements and neither fast nor slow movement speeds. Regarding individual differences, most of these preferences are robust to demographic variation, and only gender and age was found to cause slight preference differences between slow and fast movements.
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives
Faculty of Mechanical Engineering（机械工程学院）
近年来，工业机器人已被安装在各个行业，以处理先进制造和高精度任务。然而，与人类操作员相比，工业机器人的进一步集成受到其有限的灵活性，适应性和决策技能的阻碍。装配任务对机器人来说尤其具有挑战性，因为它们具有丰富的接触性，对即使很小的不确定性也很敏感。虽然强化学习（RL）提供了一个很有前途的框架，从头开始学习接触丰富的控制策略，其适用性高维连续状态动作空间仍然相当有限，由于高脆性和样本的复杂性。为了解决这些问题，我们提出了不同的修剪方法，促进收敛和推广。特别是，我们将任务分为自由和接触丰富的子任务，在笛卡尔空间而不是关节空间中执行控制，并参数化控制策略。这些修剪方法自然地在动态运动基元（dynamic movement primitives，简称HMM）的框架内实现。为了处理接触丰富的任务，我们通过引入一个耦合项来扩展RISK框架，该耦合项的作用类似于人类手腕，并在与环境接触时提供主动顺应性。我们证明了所提出的方法可以学习对空间，大小，形状和密切相关的场景不变的插入技能，同时处理大的不确定性。最后，我们证明了学习的政策可以很容易地从模拟转移到现实世界中，并实现类似的性能UR5e机器人。
JcvPCA and JsvCRP : a set of metrics to evaluate changes in joint coordination strategies
Sorbonne Université, CNRS, INSERM, Institute for Intelligent Systems and Robotics (ISIR)（索邦大学、CNRS、INSERM、智能系统与机器人研究所）；Laboratoire LINP2-2APS, UPL, Université Paris Nanterre（LINP2-2APS实验室、UPL、巴黎纳维尔大学）
Development of a passive Rehabilitation Robot for the wrist joint through the implementation of an Arduino UNO microcontroller
Universidad de Los Andes（洛斯安第斯大学）
在这项研究中，使用Arduino UNO R3微控制器来控制开发用于在腕关节中进行康复练习的原型机器人功能的运动;该设备可用于协助理疗师恢复肌腱炎，滑膜炎，类风湿性关节炎以及该关节的术前和术后治疗。在功能原型的设计阶段，从并行工程方法中使用了工业设计过程的方法，通过该方法，可以根据收集的信息对委内瑞拉人口的腕关节运动的尺寸和角度进行人体测量研究，详细说明设计方案，并使用CAD程序定义不同的形式，几何形状和材料的康复设备的组成部分，这是后来分析使用有限元法的确定张力状态的努力和安全因素，通过使用CAE程序。此外，还开发了一个软件，用于采集、记录、再现和执行康复治疗期间产生的不同运动。通过这项研究，设计了一种有助于腕关节康复的设备，允许掌背屈和尺桡骨运动相结合，以恢复委内瑞拉人口中出现的各种病理的关节功能。
