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.
Image-driven Robot Drawing with Rapid Lognormal Movements
Goldsmiths, University of London（伦敦大学金史密斯学院）；Idiap Research Institute and École Polytechnique Fédérale de Lausanne (EPFL)（日内瓦联邦理工学院（EPFL）与IDIAP研究所）；University of Konstanz（康斯坦茨大学）；Ecole Polytechnique de Montréal（蒙特利尔联邦理工学院）
Efficient and robust task planning for a human-robot collaboration (HRC) system remains challenging. The human-aware task planner needs to assign jobs to both robots and human workers so that they can work collaboratively to achieve better time efficiency. However, the complexity of the tasks and the stochastic nature of the human collaborators bring challenges to such task planning. To reduce the complexity of the planning problem, we utilize the hierarchical task model, which explicitly captures the sequential and parallel relationships of the task. We model human movements with the sigma-lo…
INESC-ID（里斯本系统与计算机工程研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）
We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e.g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot. In the first stage, we perceive and pre-process the given inputs, isolating individual commands from the complete instruction provided by a human user. In the second stage we encode the individual commands into a multimodal latent space, employing a deep generative model. Finally, in the third stage we convert the multimodal latent…
Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments
AMA University（AMA大学）；New York University（纽约大学）；University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；Northeastern University（东北大学）；Cornell University（康奈尔大学）；University of California, Irvine（加州大学欧文分校）；Northern Arizona University（北亚利桑那大学）
Communicative Efficiency of Single vs. Multi-Axis Robot Neck Motion
University of Bristol School of Engineering Mathematics and Technology（布里斯托大学工程数学与技术学院）；EPFL Institute of Mechanical Engineering（洛桑联邦理工学院机械工程学院）；University of Tokyo Department of Precision Engineering（东京大学精密工程系）
Anatomy of Uncertainty: Expressive Descriptors of Robot Motion for Nonverbal Human-Robot Communication
School Of Art, Design and Media, Nanyang Technological University（南洋理工大学艺术、设计与传媒学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航天工程学院）
