Perceiving, Modelling and Interacting with the Object-Based World

帝国理工学院戴森机器人实验室成立于2014年，是戴森科技有限公司和帝国理工学院的合作项目。这是Andrew Davison教授和戴森之间十三年合作的高潮，将他的同步定位和映射（SLAM）算法从实验室带到商业机器人中，导致戴森在2015年推出基于360 Eye视觉的真空清洁机器人，它可以映射其周围环境，定位和规划系统的清洁模式。我们合作的成功表明，计算机视觉是未来机器人的关键技术。该提案旨在资助实验室将视觉场景理解和视觉机器人操作的前沿推向新的和更苛刻的应用领域。我们在此繁荣合作伙伴关系中概述的研究活动补充了戴森正在进行的大型内部研发投资，以创造先进的机器人产品。此次合作的目标是发明和原型化突破性的机器人视觉算法，真正将我们带入下一代在非结构化环境中工作的先进机器人技术，并将该技术转移到戴森的长期产品线中，以开拓新的产品类别。戴森现在已经在机器人领域工作了近20年，在此期间，真实的消费机器人产品的出现伴随着人工智能广泛领域学术研究的惊人进展。目前，地板清洁器仍然是唯一一种在大众市场上取得重大商业成功的机器人。这可以简单地归结为消费者可能希望自主产品实现的其他更复杂的任务和杂务的难度更大。这些任务对机器人系统提出了更高的要求，以理解其复杂的3D环境及其包含的对象并与之交互。该计划将专注于创造实现下一代能力所需的研究突破。场景感知和建模能力是所有这些用例的基础，这些将是我们的研究重点，因为我们通过结合我们在基于状态的估计和机器学习方面的所有知识来开发下一代基于对象的SLAM系统背后的算法。我们还将更具体地研究训练学习系统的方法;高级视觉引导操作的方法;以及实用的上下文人机交互所需的框架。核心科学工作将具有前瞻性和学术性，但始终得到戴森合作伙伴的有力指导。

"Perceiving, Modelling and Interacting Autonomously in a Dynamic Object-Based World"The Dyson Robotics Lab at Imperial College was founded in 2014 as a collaboration between Dyson Technology Ltd and Imperial College. It is the culmination of a thirteen-year partnership between Professor Andrew Davison and Dyson to bring his Simultaneous Localisation and Mapping (SLAM) algorithms out of the laboratory and into commercial robots, resulting in Dyson's 360 Eye vision-based vacuum cleaning robot in 2015 which can map its surroundings, localise and plan systematic cleaning pattern. Our success in working together made it clear that computer vision is a key enabling technology for future robots. This proposal aims to fund the Lab to push the forefront of visual scene understanding and vision-enabled robotic manipulation into new and more demanding application areas.The research activity we are outlining in this Prosperity Partnership complements the large internal R&D investment that Dyson is making to to created advanced robotic products. The aims of this partnership are to invent and prototype the breakthrough robot vision algorithms which could truly take us to next generation capability for advanced robotics working in unstructured environments, and to transfer this technology into the long-term product pipeline of Dyson as they aim to open up new product categories.Dyson has now been working on robotics for nearly 20 years, a period during which the emergence of real consumer robotic products has happened alongside astounding progress in academic research in the broad field of AI. At the present time, floor cleaners are still the only category of mass-market robot which have achieved significant commercial success. This can be put down simply to the greater difficulty of the other more complex tasks and chores that a consumer might want an autonomous product to achieve. These tasks place much larger demands on a robotic system to understand and interact with its complicated 3D surroundings and the objects they contain. This programme will focus on creating the research breakthroughs needed to enable this next generation capability.There are scene perception and modelling competences which underly all of these use cases, and these will be our research focus as we develop the algorithms behind next-generation object-based SLAM systems by combining all of our knowledge in state-based estimation and machine learning. We will also work more specifically on the methods for training learning systems; methods for advanced vision-guided manipulation; and the frameworks needed for practical, contextual human-robot interaction. The core scientific work will be forward-looking and academic, but always with a strong guidance from our partners at Dyson.