Carfì Alessandro, Patten Timothy, Kuang Yingyi, Hammoud Ali, Alameh Mohamad, Maiettini Elisa, Weinberg Abraham Itzhak, Faria Diego, Mastrogiovanni Fulvio, Alenyà Guillem, Natale Lorenzo, Perdereau Véronique, Vincze Markus, Billard Aude
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Vision for Robotics Laboratory, Institut für Automatisierungs- und Regelungstechnik, Technische Universität Wien, Vienna, Austria.
Front Robot AI. 2021 Oct 1;8:714023. doi: 10.3389/frobt.2021.714023. eCollection 2021.
Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world.
人机交互对于机器人在人类环境中运行至关重要。然而,当前最先进的机器人手远不能复制人类的技能。因此,研究人类如何使用双手来开发类似的机器人能力至关重要。本文深入探讨了手与物体的交互以及人类演示,突出了该研究领域的主要挑战,并提出了未来理想的发展方向。在此范围内,本文给出了手与物体交互问题的一般定义,并对所涉及的每个主要子问题进行了简要综述,即:传感、感知和学习。此外,本文还讨论了这些子问题之间的相互作用,并描述了它们在从演示中学习时的交互如何有助于机器人操作的成功。通过这种方式,本文全面概述了机器人系统通过观察现实世界中的人类行为来学习新操作技能所需的跨学科方法。