Robotic skill transfer and augmentation for contact-rich tasks in manufacturing (STAMAN)

制造中接触丰富的任务的机器人技能转移和增强 (STAMAN)

• 批准号: EP/Y02270X/1
• 负责人: Yongjing Wang
• 金额: $ 125.8万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY02270X%2F1
• 关键词: Robotic; skill; transfer; augmentation; contact

Many assembly and disassembly tasks in manufacturing have small clearances and limited accessibility, such as shaft-hole insertion/separation and bolt-nut assembly/disassembly. Using robots in these contact-rich tasks is more complex than those having no physical contacts (e.g. computer visual inspection) or simple contacts (e.g. cutting, welding, pick-and-place). The deployment of robots in contact-rich tasks has been limited to date. The contact-rich tasks that involve complex shapes, small clearances or deformable materials are particularly challenging to robotise due to the likely events of jamming and wedging. Our previous research has investigated techniques that allow robots to learn contact-rich skills (e.g. complex motion plans and force control policies) using two main AI-based pathways: (1) self-learning from trial-and-error, and (2) learning from human demonstrations. The two participating universities, Birmingham and Sheffield, have research experiences in (1) and (2), respectively.A key challenge observed in the current research is that in many cases a robot's contact-rich skill cannot be performed by other robots of different motion properties (e.g. accuracy, precision and stiffness), or be applied to a new task with variations (e.g. differences in object geometry, shape, and materials). This is because a robotic contact-rich skill, i.e. control policies and motion plans, is usually acquired for a specific task and cannot be adopted by new robots or in new tasks.STAMAN's vision is to create AI-based mechanisms to allow robots to share and recreate obtained digital skills (e.g. motion and force/torque control strategies) to allow easy automation scale-up for contact-rich tasks. This includes considering two research questions: 1) For skill transfer - how can a contact-rich skill be quickly transferred to a different robot (e.g. transferring a bolt-nut separation skill from a high-precision robot to a low-precision robot)? 2) For skill augmentation - how can existing contact-rich skills be used to create new contact-rich skills (e.g. augmentation of rigid-material skills to deal with soft materials)?The project will develop a portfolio of research into the science of digital skills for contact-rich tasks, focusing on common manufacturing tasks such as bolt-nut assembly/disassembly, peg-hole insertion/separation, and shaft-ring assembly/disassembly. The ability to transfer and augment digital skills for contact-rich tasks will allow automation systems to be implemented on a larger scale, with minimal manual setting and fine-tuning required. STAMAN aims to create transferrable and augmentable digital skills that will underpin the development of mass machine skills for future manufacturing, similar to how industrial robots have contributed to modern mass production.The proposed research encourages more use of robots in assembly (e.g. automotive, aerospace, electronics, etc.) and disassembly (e.g. repairs, remanufacturing and recycling), and thus directly contributes to the UK's Made Smarter initiative and the circular economy goals.

制造业中许多组装和拆卸任务都具有较小的间隙和有限的可访问性，例如轴孔插入/分离和螺栓 - 螺栓固定组件/拆卸。在这些接触式任务中使用机器人比没有物理接触（例如计算机视觉检查）或简单接触（例如切割，焊接，拾取和位置）的机器人更为复杂。迄今为止，机器人在接触率丰富的任务中的部署已限制。涉及复杂形状，较小的间隙或可变形材料的接触式任务尤其具有挑战性，这对于可能的干扰和楔子而言可能是挑战性的。我们以前的研究研究了允许机器人使用两个基于AI的基于AI的途径学习接触技巧（例如复杂的运动计划和力量控制策略）的技术：（1）从反复试验中进行自我学习，以及（2）从人类示范中学习。 The two participating universities, Birmingham and Sheffield, have research experiences in (1) and (2), respectively.A key challenge observed in the current research is that in many cases a robot's contact-rich skill cannot be performed by other robots of different motion properties (e.g. accuracy, precision and stiffness), or be applied to a new task with variations (e.g. differences in object geometry, shape, and materials).这是因为一种机器人的接触技巧，即控制政策和运动计划，通常是用于特定任务的，而新机器人或新任务都不能采用。Staman的愿景是创建基于AI的机制，以允许机器人共享和重新培养获得的数字技能（例如，运动和力量控制策略），以便容易自动化自动化量表。这包括考虑两个研究问题：1）用于技能转移 - 如何将接触率丰富的技能迅速转移到其他机器人（例如将螺栓-NUT分离技能从高精度机器人转移到低精确机器人）？ 2）对于技巧增强 - 如何使用现有的富含接触技巧来创建新的接触技巧（例如，增强刚性材料的技能来处理软材料）？该项目将开发针对数字技能科学的研究组合，以涉及接触型任务的数字技能科学，重点介绍了BOLT-NUT组装工作，例如BOLT-NUT组装/分离/分离型组装/固定型/固定型，并将其组装，固定型和竖井。传输和增强数字技能以完成接触式任务的能力将使自动化系统更加规模实施，并且需要最少的手动设置和微调。耐力旨在创造可转让且可增强的数字技能，这将构成与工业机器人如何促进现代大规模生产的贡献的大众机器技能的发展。拟议的研究鼓励在组装中更多地使用机器人（例如自动，航空航天，航空，电子学等），以及对公共造成的贡献，以及贡献，并贡献了Recrairs，以及Remanubled和Remcrand和Remcring and remcrand and Cruptring and cormanting and concrand and concring and concring and concring y。倡议和循环经济目标。