Learning robot navigation and manipulation from demonstrations

目的：-研究并实现一种用于移动机器人执行与其领域无关的复杂导航任务的演示学习导航方法。-研究实现移动机械臂机器人执行复杂操作任务以及独立于机器人领域的复杂导航任务的导航演示学习方法。-使用所研究的演示学习方法在通常由操作员远程操作的移动机械手机器上实现自主性。当前的自主系统方法是特定于领域的。因此，操作者仍然负责机器人的移动。本研究研究从演示中学习(LFD)，以使相同的遥操作机器可以被转换为自主执行。当机器人从演示中学习任务时，它必须从控制数据中的噪声中破译出有用的任务信息。该研究不仅扩展到能够重放演示，而且扩展到根据机器人环境中的变化来调整任务的执行。移动机器人和移动机械手的LFD方法已经存在，但是这些方法不能概括任务并且依赖于机器人的系统动力学是已知的。他们还使用昂贵的传感器，如激光雷达，而不是相机传感器。我要研究的LFD方法，并在移动机器人和移动机械手上实施，是受到Amir Ghalamzan博士对机械手机器人的研究的启发。然而，将现有的机械手机器人模型重新映射到移动机器人和移动机械手上并不足以使这些机器人完全自主。我将进一步研究最先进的深度学习方法，使机器人不仅模仿或模仿演示的任务。但能够生成模拟演示的方法，并在学习任务时包含这些演示。这个想法是为了改善任务的执行，并能够推广到独立于机器人的领域。研究的结果是产生一种在移动机器上实现自主性的计算高效和有效的方法。机器人的LFD的人类可重新编程的性质将提高机器人的适应水平，因为机器人专家将不需要不断地对机器人进行重新编程。

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.