Autonomous Motor Skills for Versatile Dynamic Legged Locomotion in Realistic Scenarios

该项目旨在利用最先进的机器学习技术优化有腿机器人的运动能力。这些技术的使用允许有腿的机器人通过最有效的方法学习，这将有助于机车应用，以增强动作，步态和整体稳定性。将深度强化学习算法引入控制系统还可以改善机器人共存组件与周围环境之间的动态交互，以改进和维持支持理想输出的稳定状态。系统生成的智能决策将通过深度神经网络采用，以创建基于记忆的功能，这些功能可以离线优化，进一步允许物理机器人在现实场景和环境下继承它们。随后，在机器人中使用人工智能的愿景不仅有助于在宪法系统内进行运动控制，而且还会引导识别周围区域以检测物体和地形的把握，以进一步补充步态，以保持机车效率。

The project is aiming to optimise the locomotion capabilities of legged robots using the state-of-art machine learning technologies. The uses of such technologies allow the legged robot to learn through the most efficient methods, which in conclusion will aid with locomotive application in order to enhance actions, gaits and overall stability. Inducing deep reinforcement learning algorithms into the control system also improves the dynamic interaction between the coexisting components of robots in parallel with the surrounding environment in order to refine and maintain a stabilised state supporting a desirable output. The systematically generated intelligent decisions will be adopted through deep neural networks in order to create memory-based functions, which can be optimised off-line and furthermore allow physical robots to inherit them under realistic scenarios and environments. Subsequently, the vision of the use of AI within the robots will not just aid with the motion control within the constitutional system but also induce a grasp of identifying the surrounding area to detect objects and terrain to further supplement the gait in order to maintain locomotive efficiency.