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.

制造中的许多组装和拆卸任务间隙较小且可及性有限，例如轴孔插入/分离以及螺栓螺母组装/拆卸。在这些富含接触的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、拾取和放置）的任务更复杂。迄今为止，机器人在接触丰富的任务中的部署仍然受到限制。由于可能发生卡住和楔入事件，涉及复杂形状、小间隙或可变形材料的接触丰富的任务对于机器人化来说尤其具有挑战性。我们之前的研究调查了允许机器人使用两种主要的基于人工智能的途径学习接触丰富的技能（例如复杂的运动计划和力控制策略）的技术：（1）从试错中自我学习，以及（2）从人类演示中学习。伯明翰和谢菲尔德两所参与大学分别在（1）和（2）方面拥有研究经验。当前研究中观察到的一个关键挑战是，在许多情况下，机器人的丰富接触技能无法由具有不同运动特性（例如精度、精度和刚度）的其他机器人执行，也不能应用于具有变化的新任务（例如物体几何形状、形状和材料的差异）。这是因为机器人富含接触的技能，即控制策略和运动计划，通常是针对特定任务而获得的，并且不能被新机器人或新任务采用。STAMAN的愿景是创建基于人工智能的机制，使机器人能够共享和重新创建获得的数字技能（例如运动和力/扭矩控制策略），从而轻松实现接触丰富任务的自动化扩展。这包括考虑两个研究问题：1）对于技能转移——如何将接触丰富的技能快速转移到不同的机器人（例如，将螺栓螺母分离技能从高精度机器人转移到低精度机器人）？ 2) 对于技能增强——如何利用现有的接触丰富的技能来创建新的接触丰富的技能（例如，增强刚性材料技能以处理软材料）？该项目将开发一系列针对接触丰富的任务的数字技能科学的研究组合，重点关注常见的制造任务，例如螺栓螺母组装/拆卸、钉孔插入/分离以及轴环组装/拆卸。为接触丰富的任务传输和增强数字技能的能力将使自动化系统能够在更大范围内实施，并且需要最少的手动设置和微调。 STAMAN 旨在创造可转移和可增强的数字技能，以支持未来制造业大规模机器技能的发展，类似于工业机器人对现代大规模生产的贡献。拟议的研究鼓励在装配（例如汽车、航空航天、电子等）和拆卸（例如维修、再制造和回收）中更多地使用机器人，从而直接为英国的“变得更聪明”做出贡献 倡议和循环经济目标。