Imitation learning for contact-rich manipulation

模仿学习进行丰富的接触操作

• 批准号: 2901317
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2901317
• 关键词: Imitation; learning; contact; rich; manipulation

Currently, our goal is to extend the CTF IL framework [6] to handle contact-rich tasks byimproving the way object interaction occurs. The objective is to create a method that accountsfor force feedback as well as being closed-loop in order to adapt to errors arising during contactor from the visual feedback that can occur when approaching the object. Initially, we are focusingon scenarios where a new demonstration is provided for each new object, where providing arepresentative demonstration to solve the task is possible. Then, in the future we aim to exploremethods that build control policies based on 3D object models constructed directly from vision andtasks that require tactile feedback to be solved. Additionally, we aim to extend this to be able togeneralise demonstrations across similar objects and utilise prior knowledge obtained either in thereal-world or simulation to provide solutions for tasks were providing a clear enough demonstrationis not possible. Looking forward, the ambition is that by the end we can have a framework wherehumans can teach robots to solve complex, contact-rich manipulation tasks in the real-world, withonly a handful of demonstrations and improve from interactive human feedback.General research area: robotics

目前，我们的目标是扩展CTF IL框架[6]，通过改进对象交互的方式来处理接触丰富的任务。我们的目标是创建一种既考虑力反馈又闭环的方法，以便适应接触过程中因接近物体时可能发生的视觉反馈而产生的错误。最初，我们专注于为每个新对象提供新演示的场景，其中提供代表性演示来解决任务是可能的。然后，在未来，我们的目标是探索方法，建立控制政策的基础上，直接从视觉和任务，需要触觉反馈来解决的3D对象模型。此外，我们的目标是扩展这一点，以便能够概括类似对象的演示，并利用在现实世界或模拟中获得的先验知识，为无法提供足够清晰演示的任务提供解决方案。展望未来，我们的目标是，到最后，我们可以有一个框架，人类可以教机器人解决复杂的，接触丰富的操作任务在现实世界中，只有少数的演示和改善互动的人类反馈。一般研究领域：机器人