In this work, we present the design, development, and experimental validation of a custom-built quadruped robot, Ask1. The Ask1 robot shares similar morphology with the Unitree Go1, but features custom hardware components and a different control architecture. We transfer and extend previous reinforcement learning (RL)-based control methods to the Ask1 robot, demonstrating the applicability of our approach in real-world scenarios. By eliminating the need for Adversarial Motion Priors (AMP) and reference trajectories, we introduce a novel reward function to guide the robot's motion style. We demonstrate the generalization capability of the proposed RL algorithm by training it on both the Go1 and Ask1 robots. Simulation and real-world experiments validate the effectiveness of this method, showing that Ask1, like the Go1, is capable of navigating various rugged terrains.

翻译：本文介绍了一款自主研发的四足机器人Ask1的设计、开发与实验验证。Ask1机器人在形态上与Unitree Go1相似，但采用了定制化的硬件组件和不同的控制架构。我们将先前基于强化学习（RL）的控制方法迁移并扩展到Ask1机器人上，证明了该方法在真实场景中的适用性。通过摒弃对抗运动先验（AMP）和参考轨迹，我们引入了一种新颖的奖励函数来引导机器人的运动风格。我们通过在Go1和Ask1机器人上训练所提出的RL算法，展示了其泛化能力。仿真和真实世界实验验证了该方法的有效性，表明Ask1与Go1一样，能够在各种崎岖地形中行进。