用强化学习让自研四足机器人学会自主行走，无需预设轨迹。
Ask1: Development and Reinforcement Learning-Based Control of a Custom Quadruped Robot
- 基于强化学习设计新奖励函数，不依赖参考轨迹和对抗性运动先验。
- 在仿真与真实场景中验证，机器人可稳定穿越复杂地形。
- 算法在Go1和Ask1间通用，适合机器人控制研究者参考。
本文介绍了一款自研四足机器人Ask1的设计、开发与实验验证。Ask1的形态与Unitree Go1相似，但采用定制硬件与不同控制架构。我们将其应用于基于强化学习（RL）的控制方法，通过消除对抗性运动先验（AMP）和参考轨迹的依赖，提出一种新型奖励函数以引导运动风格。通过在Go1与Ask1上共同训练，验证了该算法的泛化能力。仿真与真实世界实验均表明，Ask1具备在多种崎岖地形中自主移动的能力，证实了该方法在实际场景中的有效性。
原文摘要 · Abstract (English)
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.
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