Learning robot navigation and manipulation from demonstrations

通过演示学习机器人导航和操作

• 批准号: 2601734
• 金额: --
• 依托单位: University of Lincoln
• 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-2601734
• 关键词: Learning; robot; navigation; manipulation; demonstrations

Objectives:- To research and implement a Learning from demonstrations method of navigation for mobile robot to perform complex navigation tasks independent of their domain.- To research implement a Learning from demonstrations method of navigation for mobile manipulator robots to perform complex manipulation tasks as well as complex navigation tasks independent of a robot's domain.- To implement autonomy on a mobile manipulator machine, which is usually teleoperated by an operator, using the researched Learning from demonstrations methods.Humans teleoperate machines to perform mobile navigation and manipulation tasks. Current autonomous system approaches are domain specific. Therefore, human operators are still in charge of the movement of their robots.This research studies Learning from demonstrations (LfDs) so the same teleoperated machines can be transformed to perform autonomously.The proposed research involves taking quantitative control data from human demonstrations. While a robot is learning a task from a demonstration, it must decipher useful task information from the noise in the control data. The research extends to not only just being able to replay the demonstration, but to also to adapting the execution of the task according to variations within the robot's environment.LfDs methods for mobile robots and mobile manipulators already exist, however these methods do not generalise the task and depend on the robot's system dynamics being known. They also use sensors which are expensive such as LIDAR rather than camera sensors. The LfD methods I would research into, and implement on mobile robots and mobile manipulators, is inspired from the work into manipulator robots conducted by Dr. Amir Ghalamzan. However, remapping of the existing models for manipulator robots onto the mobile robots and mobile manipulators will not be enough to make these robots fully autonomous.I will be looking further into state-of-the-art deep learning methods so that the robots do not only mimic or imitate the demonstrated task. But be able to generate ways of emulating demonstrations and include those demonstrations when learning the task. The idea is to improve the execution of the task and be able to generalise to be independent of the robot's domain.The outcome of the research is to produce a computationally efficient and effective method of implementing autonomy on mobile machines. The human re-programmable nature of the LfDs for the robots will increase the level of robot adaptation, as robot experts will not be required to continually reprogram the robots.

目的： - 研究和实施从移动机器人导航的示威方法中进行学习，以执行独立于其领域的复杂导航任务。-以研究从移动操纵机器人的导航方法中进行学习，以进行复杂的操纵任务以及复杂的操纵任务以及复杂的导航任务。方法。人类teleoporate机器执行移动导航和操纵任务。当前的自主系统方法是特定领域的。因此，人类操作员仍负责其机器人的运动。这项研究从示威活动（LFD）学习（LFD），因此可以转换相同的遥控机器以自主性执行。拟议的研究涉及从人类示范中获取定量控制数据。当机器人正在从演示中学习任务时，它必须从控制数据中的噪声中解解有用的任务信息。该研究不仅扩展到能够重播演示，而且还可以根据机器人环境中的变化来调整任务的执行。移动机器人和移动操纵器的LFDS方法已经存在，但是这些方法并未概括任务并依赖于已知的机器人系统动力学。他们还使用昂贵的传感器，例如LiDar而不是相机传感器。我将研究并在移动机器人和移动操纵器上实施的LFD方法，它的灵感来自Amir Ghalamzan博士进行的操纵机器人。但是，将机器人机器人的现有模型重新映射到移动机器人和移动操纵器上不足以使这些机器人完全自动自主。我将进一步研究最先进的深度学习方法，以便机器人不仅模仿或模仿所示的任务。但是，能够生成模拟演示的方法，并在学习任务时包括这些演示。这个想法是改善任务的执行并能够概括以独立于机器人的领域。研究的结果是产生一种对移动机器实施自治的计算高效有效的方法。 LFD对于机器人的可重新编程性质将提高机器人适应水平，因为机器人专家不需要连续重新编程机器人。