Learning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Robotics, Perception and Learning (RPL), EECS, Royal Institute for Technology (KTH), Stockholm, Sweden.
Science. 2019 Jun 21;364(6446). doi: 10.1126/science.aat8414.
Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.
灵巧操作是机器人的主要目标之一。具有这种能力的机器人可以分拣和包装物品、切菜和叠衣服。随着机器人开始与人类并肩工作,它们还必须具备人类意识。在过去的十年中,研究在这些目标上取得了进展。进展来自于视觉和触觉感知的进步,以及软执行器形式的机械学进步,软执行器提供了自然的顺应性。值得注意的是,机器学习的巨大进步被用来封装不确定性模型,并支持自适应和鲁棒控制的改进。如何使机器人能够应对所有最不可预测的主体——人类,这方面仍存在一些悬而未决的问题。