Learning robot navigation and manipulation from demonstrations

目的：研究并实现一种基于演示学习的移动机器人导航方法，使其能够独立于其领域执行复杂的导航任务。-研究实现移动机械臂机器人的演示学习导航方法，以完成复杂的操作任务以及独立于机器人领域的复杂导航任务。-利用研究的演示学习方法，实现通常由操作员远程操作的移动机械手的自主性。人类遥控机器来执行移动导航和操作任务。当前的自治系统方法是特定于领域的。因此，人类操作员仍然负责机器人的运动。这项研究研究了从演示中学习（lfd），因此同样的远程操作机器可以转换为自主执行。拟议的研究包括从人类示范中获取定量控制数据。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有用的任务信息。这项研究不仅扩展到能够回放演示，而且还扩展到根据机器人环境的变化来调整任务的执行。针对移动机器人和移动机械手的lfd方法已经存在，但是这些方法不能泛化任务，并且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，如激光雷达，而不是相机传感器。我将研究并在移动机器人和移动机械手上实施的LfD方法，是受到Amir Ghalamzan博士对机械手机器人的研究的启发。然而，将现有的机械臂机器人模型重新映射到移动机器人和移动机械臂上，还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任务。但是能够生成模拟演示的方法，并在学习任务时包含这些演示。这个想法是为了提高任务的执行能力，并能够独立于机器人的领域进行归纳。研究的结果是产生一种计算效率高且有效的方法来实现移动机器的自治。机器人lfd的人类可重新编程特性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重新编程机器人。

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.