Grasping Grocery Items with Sensorised Robotic Manipulators and Reinforcement Learning

使用传感机器人操纵器和强化学习抓取杂货

• 批准号: 2425110
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2425110
• 关键词: Grasping; Grocery; Items; Sensorised; Robotic

Robotic manipulation remains an unsolved challenge in fully autonomous industrial settings, such as automated supermarkets (according to Ocado - the world's largest online-only supermarket). Successful solutions must enable robotic systems to operate robustly in uncertain, unstructured, and dynamic environments. Thus, methodologies inspired by Nature, i.e. humans and animals, may playthe ideal role model, in particular, for robotic grasping and manipulation systems Karmakar et al., 2019. Application dependent solutions are required able to cope with different types and shapes of objects on a day-today basis. The use-cases are vast - a challenging example, though, involves theindividually sorting and packing of warehouse products in a fully automatically way Sotiropoulos et al., 2018. One solution to this problem may be based off robotic manipulators Bogue et al., 2016. The rapidly growing research on soft robotics has seen its application in fields including human-robot interaction Arnold et al., 2017 allowing inherently safe collaboration with humans Endo et al., 2013. The idea of robotic softhand manipulation may help in tackling some of these presented challenges. Projects, such as SoMa(Soft Manipulation) 6, with a close collaboration between universities and industry, attracted multimillion investment of the European funding under Horizon 2020, to benchmark several soft grippers andhands for fruit grasping tasks. Robotic end effectors proposed for handling irregularly shaped, soft and easily damaged goods (e.g., fruits and vegetables) are currently divided from state of the art grasping with machine learning. Soft grippers achieve best performance on these grocery items Friedl et al. 2020, Angeliniet al., Mnyusiwalla et al. 2020; however, the most effective machine learning based approaches only investigate rigid parallel-jaw grippers or suction [Levine et al., 2018, Mahler et al., 2019]. There is a need to investigate new robotic end-effectors that are suited for combing both soft behaviour and machine learning approaches.This PhD research thesis will focus on a developing a grasping manipulator that uses sensorial feedback control (i.e.tactile sensors as touch sensing) to complete robust and effectivehandling of irregularly shaped, soft and easily damaged goods. This gripper will integrate with machine learning techniques, notably being suited for accurate simluation. Such a project will be timely and innovative as it will deliver positive economic impact in the automation of the foreseen future. We plan designing novel hardware for applications that require high complexity in the end to-end research pipeline. We will focus on coupling the design with the required automation, such as task planning and collision-free path planning, using advanced perception systems through both geometrical modelling of the environment and learning techniques.The unique focus of the project that will guarantee a successful implementation, is the integration ofRobotics and AI expertise in the field for as close cross-departmental collaboration of two thriving UCL university lecturers Dr. Dimitrios Kanoulas (Lecturer in Robotic Sensing and Manipulation atUCL Computer Science) and Dr. Helge Wurdemann (Lecturer in Robotics and Haptics at UCL Mechanical Engineeing). Dr. Kanoulas offers extensive research experience in the field of perception and learning and would advise the algorithmic implementation and analysis of the project proposal, while Dr.Wurdemann, with his expertise in the creation of novel and innovative soft robotic systems, will advise on the design of the soft-hand manipulator and implementation.

机器人操作在完全自主的工业环境中仍然是一个未解决的挑战，例如自动化超市（根据Ocado -世界上最大的在线超市）。成功的解决方案必须使机器人系统能够在不确定的、非结构化的和动态的环境中稳健地运行。因此，受自然启发的方法，即人类和动物，可以扮演理想的角色模型，特别是对于机器人抓取和操纵系统Karmakar等人，2019.依赖于应用的解决方案需要能够每天科普不同类型和形状的对象。使用案例是巨大的-一个具有挑战性的例子，虽然，涉及单独分拣和包装的仓库产品在一个完全自动的方式Sotiropoulos等人，2018.这个问题的一个解决方案可以基于机器人操纵器Bogue等人，2016.软机器人的研究迅速增长，已经在包括人-机器人交互在内的领域中得到应用。2017年允许与人类进行固有安全的合作Endo等人，2013.机器人软手操作的想法可能有助于解决这些挑战。索马（Soft Manipulation）6等项目与大学和行业密切合作，在地平线2020下吸引了数百万欧洲资金的投资，以基准测试几种用于水果抓取任务的软抓取器和手。提出了用于处理不规则形状、柔软和容易损坏的货物（例如，水果和蔬菜）目前与利用机器学习进行抓取的现有技术分开。软夹持器在这些食品杂货上实现最佳性能Friedl et al. 2020，Angeliniet al.，Mnyusiwalla等人，2020年;然而，最有效的基于机器学习的方法仅研究刚性平行钳口夹持器或吸力[Levine等人，2018年，Mahler等人，2019年]。有必要研究适合结合软行为和机器学习方法的新型机器人末端执行器。本博士研究论文将专注于开发一种抓取机械手，该机械手使用感觉反馈控制（即触觉传感器作为触摸传感）来完成不规则形状，柔软和易损坏的货物的鲁棒性和有效性处理。这个夹具将与机器学习技术相结合，特别是适合精确的模拟。这样的项目将是及时和创新的，因为它将在可预见的未来自动化方面产生积极的经济影响。我们计划为在端到端研究管道中需要高复杂性的应用设计新型硬件。我们将专注于将设计与所需的自动化相结合，例如任务规划和无碰撞路径规划，使用先进的感知系统，通过环境的几何建模和学习技术。该项目的独特重点将确保成功实施，是机器人和人工智能专业知识在该领域的整合，两个蓬勃发展的UCL大学讲师Dimitrios Kanoulas博士（UCL计算机科学机器人传感和操纵讲师）和Helge Wurdemann博士（UCL机械工程机器人和触觉讲师）的部门合作。Kanoulas博士在感知和学习领域拥有丰富的研究经验，并将为项目提案的算法实施和分析提供建议，而Wurdemann博士凭借其在创造新颖和创新的软机器人系统方面的专业知识，将为软手机械手的设计和实施提供建议。

项目成果

Reinforcement Learning-Based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language-Image Learning

• DOI: 10.1109/sii58957.2024.10417178
• 发表时间: 2024-01
• 期刊: 2024 IEEE/SICE International Symposium on System Integration (SII)
• 作者: Xiang Long;Luke Beddow;Denis Hadjivelichkov;Andromachi Maria Delfaki;Helge A Wurdemann;D. Kanoulas