Deep Reinforcement learning of control and manipulation tasks using a tactile robotic hand.

使用触觉机器人手对控制和操作任务进行深度强化学习。

• 批准号: 2177732
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2177732
• 关键词: Deep; Reinforcement; learning; control; manipulation

In my proposed research I will explore the application of state of the art, supervised and unsupervised machine learning methods in the task of control and manipulation of a tactile robotic hand. Emphasis will be put on utilising deep reinforcement learning methods, under the co-supervision of area expert Dr Raia Hadsell of Google DeepMind.Deep reinforcement learning is a relatively unexplored area, particularly in its application to tactile sensing or robotics in general. It can be described as bridging the divide between high-dimensional sensory inputs and actions, which perfectly fits the proposed tasks of control and manipulation with tactile sensing. High dimensional sensory inputs are provided through images capturing tactile data, whilst actions can be a variety of control or manipulation tasks i.e. the movement of joints in a robotic hand.For the initial phase of the PhD the focus will be put on gaining a better understanding of how deep learning methods apply to a wider range of tasks based around a tactile sensor. This will lead into applying the methods to a full, sensorized tactile robotic hand, which will benefit from other work in the lab. The next phase will be to extend into unsupervised methods where deep reinforcement learning shows the most promise; groundwork for this methodology has been presented but this has yet to be realised with the TacTip or similar optical tactile sensing technology.

在我提出的研究中，我将探索最先进的、有监督的和无监督的机器学习方法在控制和操纵触觉机器人手任务中的应用。在b谷歌DeepMind领域专家Raia Hadsell博士的共同监督下，重点将放在利用深度强化学习方法上。深度强化学习是一个相对未开发的领域，特别是在触觉传感或机器人方面的应用。它可以被描述为弥合高维感官输入和行动之间的鸿沟，这完全符合触觉感知的控制和操作任务。高维感官输入通过捕捉触觉数据的图像来提供，而动作可以是各种控制或操作任务，即机器人手中的关节运动。在博士学位的初始阶段，重点将放在更好地理解深度学习方法如何应用于基于触觉传感器的更广泛的任务上。这将导致将这些方法应用于一个完整的、感应触觉的机器人手，这将受益于实验室的其他工作。下一阶段将扩展到无监督方法，其中深度强化学习显示出最有希望；这种方法的基础已经提出，但这还没有实现与战术或类似的光学触觉传感技术。