MAN^3: huMAN-inspired robotic MANipulation for advanced MANufacturing

在过去的50年里，机器人在工业中的使用单调地增加，在过去的10年里，它真正蓬勃发展。2016年，全球制造业的平均机器人密度（即每万名员工的机器人数量）为74个；按地区划分，欧洲为99台，美洲为84台，亚洲为63台，亚洲的平均年增长率（2010年至2016年）为9%，美洲为7%，欧洲为5%。从2018年到2020年，全球机器人安装预计平均每年至少增长15%。到目前为止，主要市场是汽车行业（即重型制造业的一个例子），在那里，简单和重复的机器人操作任务是由大型和昂贵的机器人在非常受控的环境中执行的，在工厂的专用区域，人类工人出于安全原因不允许进入。机器人的新增长市场是消费电子和食品/饮料（例如轻工制造业）以及其他中小型企业（sme）：特别是食品和饮料行业的机器人订单在2011年至2015年期间每年增长12%，2016年增长20%。然而，在许多情况下，这些行业的生产过程需要对几个不同的项目进行精细的处理和精细的操作，这对当前商业机器人系统的能力提出了严峻的挑战。2016年，英国每万名员工拥有71台机器人，是七国集团中唯一一个机器人密度低于74台世界平均水平的国家，排名第22位。工业和中小企业部门非常需要现代化，这将提高生产力，改善人类工人的工作条件（例如安全，参与）：这需要开发和部署新的机器人技术，以满足那些目前机器人尚未有效的企业的需求。机器人在这些应用中效果不佳的主要原因之一是缺乏机器人智能：即人类典型的学习和适应能力。事实上，机器人操作可以通过依赖人类来增强，既可以通过互动（即人类作为直接老师），也可以通过灵感（即人类作为模型）。因此，本项目的目的是开发一个系统，用于自然人类演示机器人操作任务，将沉浸式虚拟现实技术和智能可穿戴设备（将人与机器人连接起来）与机器人感觉运动学习技术和多模态人工感知（受人类感觉运动系统的启发）相结合。机器人系统将包括一组传感器，可以重建现实世界，特别是通过将3D视觉与接触的触觉信息相结合；人类用户将通过结合视觉和触觉反馈的沉浸式虚拟现实来访问这种人工重建。换句话说，用户将通过机器人的眼睛去看，通过机器人的手去感受。此外，用户将能够通过移动自己的四肢来移动机器人。这将允许人类用户轻松地向机器人教授复杂的操作任务，机器人从人类演示中学习有效的控制策略，以便他们可以在未来自主地重复任务。人类演示简单的机器人任务已经找到了它的工业方式（例如机器人绘画，简单的拾取和放置刚性物体），但它仍然不能应用于灵巧处理一般物体（例如柔软和精致的物体），这将导致更大的适用性（例如食品处理）。因此，该项目的预期结果将提高大量工业过程的生产率（经济影响），并在安全和参与方面改善人类工人的工作条件和生活质量（社会影响）。

Across the past 50 years, the use of robots in industry has monotonically increased, and it has literally boomed in the last 10 years. In 2016, the average robot density (i.e. number of robot units per 10,000 employees) in the manufacturing industries worldwide was 74; by regions, this was 99 units in Europe, 84 in the Americas and 63 in Asia, with an average annual growth rate (between 2010 and 2016) of 9% in Asia, 7% in the Americas and 5% in Europe. From 2018 to 2020, global robot installations are estimated to increase by at least 15% on average per year. The main market so far has been the automotive industry (i.e. an example of heavy manufacturing), where simple and repetitive robotic manipulation tasks are performed in very controlled settings by big and expensive robots, in dedicated areas of the factories where human workers are not allowed to enter for safety reasons. New growing markets for robots are consumer-electronics and food/beverages (i.e. examples of light manufacturing) as well as other small and medium sized enterprises (SMEs): in particular, the food and beverage industry has increased robot orders by 12% each year between 2011 and 2015, and by 20% in 2016. However, in many cases the production processes of these industries require delicate handling and fine manipulations of several different items, posing serious challenges to the current capabilities of commercial robotic systems. With 71 robot units per 10,000 employees (in 2016), the UK is the only G7 country with a robot density below the world average of 74, ranking 22nd. The industry and SME sector is highly in need of a modernization that would increase productivity and improve the working conditions (e.g. safety, engagement) of the human workers: this requires the development and deployment of novel robotic technologies that could meet the needs of those businesses in which current robots are yet not effective. One of the main reasons why robots are not effective in those applications is the lack of robot intelligence: the ability to learn and adapt that is typical of humans. Indeed, robotic manipulation can be enhanced by relying on humans, both through interaction (i.e. humans as direct teachers) and through inspiration (i.e. humans as models). Therefore, the aim of this project is to develop a system for natural human demonstration of robotic manipulation tasks, combining immersive Virtual Reality technologies and smart wearable devices (to interface the human with the robot) with robot sensorimotor learning techniques and multimodal artificial perception (inspired by the human sensorimotor system). The robotic system will include a set of sensors that allow to reconstruct the real world, in particual by integrating 3D vision with tactile information about contacts; the human user will access this artificial reconstruction through an immersive Virtual Reality that will combine both visual and haptic feedback. In other words, the user will see through the eyes of the robot, and will feel through the hands of the robot. Also, users will be able to move the robot just by moving their own limbs. This will allow human users to easily teach complex manipulation tasks to robots, and robots to learn efficient control strategies from the human demonstrations, so that they can then repeat the task autonomously in the future.Human demonstration of simple robotic tasks has already found its way to industry (e.g. robotic painting, simple pick and place of rigid objects), but still it cannot be applied to the dexterous handling of generic objects (e.g. soft and delicate objects), that would result in a much larger applicability (e.g. food handling). Therefore, the expected results of this project will boost productivity in a large number of industrial processes (economic impact) and improve working conditions and quality of life of the human workers in terms of safety and engagement (social impact).