Imitation learning for contact-rich manipulation

目前，我们的目标是通过改进对象交互发生的方式来扩展CTF IL框架[6]来处理联系人丰富的任务。目标是创建一种既考虑力反馈又考虑闭环性的方法，以便适应接触器过程中因接近对象时可能发生的视觉反馈而产生的误差。最初，我们专注于为每个新对象提供新演示的场景，其中提供具有代表性的演示来解决任务是可能的。然后，在未来，我们的目标是探索基于直接从视觉构建的3D对象模型和需要解决的触觉反馈的任务来构建控制策略的方法。此外，我们的目标是扩展这一点，使其能够推广到类似对象上的演示，并利用在真实世界或模拟中获得的先验知识为任务提供解决方案，这些任务提供了足够清晰的演示，这是不可能的。展望未来，我们的雄心是到最后可以有一个框架，在这个框架中，人类可以教机器人在现实世界中解决复杂的、接触丰富的操作任务，只需几个演示，并通过交互的人类反馈来改进。一般研究领域：机器人学

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