启发式算法与机器学习的区别_使用强化学习训练受启发的四足机器人

启发式算法与机器学习的区别

It’s been a while since I’ve started exploring Reinforcement Learning and OpenAI Gym , inspired by the amazing Boston Dynamics Spot. I’ve spent last year studying the foundations of Machine Learning and how it is applied to robotics discovering a very interesting world.

自从我开始探索《 Reinforcement Learning和OpenAI Gym以来已经有一段时间了，灵感来自令人惊叹的Boston Dynamics Spot。 去年，我一直在研究机器学习的基础，以及如何将其应用于发现非常有趣的世界的机器人技术。

In order to experiment what I’ve learned, I’ve created an open-source project called rex-gym. The aim is to let an open-source quadruped robot learns domestic and generic tasks in the simulations and then successfully transfer the knowledge (Control Policies) on the real robot without any other manual tuning.

为了实验我学到的东西，我创建了一个名为rex-gym的开源项目。 目的是让开放源代码的四足机器人在模拟中学习家庭任务和通用任务，然后在不进行任何其他手动调整的情况下，成功地在真实机器人上转移知识( Control Policies )。

Rex is a 12 joints robot with 3 motors (Shoulder, Leg and Foot) for each leg. The robot base model is imported in pyBullet using a URDF file and the servo motors are modelled in the motor class.

雷克斯(Rex)是一个12关节机器人，每条腿有3个电机( Shoulder ， Leg和Foot )。 使用URDF文件将机器人base模型导入pyBullet并在motor类中对伺服电动机进行建模。

There is also an enhanced version that comes with a 6DOF robotic arm mounted on the top of the rack

还有一个增强版本，在机架顶部装有6DOF机械臂

Rex with a 6DOF robotic arm 带有6DOF机械臂的