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

在我提出的研究中，我将探索最先进的有监督和无监督机器学习方法在触觉机器人手的控制和操纵任务中的应用。在Google DeepMind的领域专家赖亚·哈德塞尔博士的共同监督下，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了高维感官输入和动作之间的鸿沟，完美地满足了拟议的触觉控制和操纵任务。通过捕获触觉数据的图像提供高维感官输入，而动作可以是各种控制或操纵任务，即机器人手中关节的运动。对于博士学位的初始阶段，重点将放在更好地了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。这将导致将这些方法应用于完整的传感器化触觉机器人手，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，深度强化学习显示出最大的希望;这种方法的基础已经提出，但这还没有通过TacTip或类似的光学触觉传感技术实现。

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.