Learning to walk: Reinforcement Learning for robotic locomotion

运动是任何自主机器人或系统想要在其环境中产生重大变化所必需的基本技能。只有最简单的生产线机器人才能完全从固定位置运行。然而，运动也是最多样化和最具挑战性的技能之一，需要以稳健和普遍的方式学习。在自然界中有无数不同的运动策略的例子，它们几乎都需要大量的时间投入来掌握。在机器人技术中超越轮式运动的尝试往往是非常脆弱的。当地形的材料特性出乎意料或表面不规则时，机器人往往无法适应。该项目旨在通过试错强化学习学习稳健和自适应行走策略来解决这一差距。该项目将探索一系列奇特的运动策略，从混合轮腿到海星式六足机器人。在这些场景中，机器人将尝试根据感知和自己的运动行为来估计它正在移动的表面的属性。然后，它会将此作为条件反射信息，告知接下来的运动策略。该项目将专门探索符号强化学习作为快速运动的高效控制回路，也可能涉及自动化机器人设计领域，涉及学习最佳运动硬件。

Locomotion is a fundamental skill necessary for any autonomous robot or system which wants to effect significant changes within it's environment. Only the most simplistic production line robotics can operate entirely from a fixed location. However, locomotion is also one of the most varied andchallenging skills to learn in a robust and generalisable way. In nature there are countless examples of different locomotion strategies, and they almost universally require a huge time investment to master. Attempts to go beyond wheeled locomotion in robotics tend to be very brittle. The robot often cannot adapt when the material properties of the terrain are unexpected, or the surface is irregular. This project will aim to address this gap by learning robust and adaptive walking strategies through trial and error reinforcement learning. The project will explore a range of exotic locomotion strategies,from hybrid wheel-legs to starfish-like hexapod robots. In these scenarios the robot will attempt to estimate the properties of the surface it is moving across based on both perception, and it's own locomotion behaviours. It will then use this as conditioning information to inform the following locomotion strategy. The project will specifically explore symbolic-RL as a high efficiency control loop for rapid locomotion, and may also touch on areas of automated robotic design, with regards to learning the optimal locomotion hardware.