论文标题
重新审视动力随机性：四足动力的案例研究
Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion
论文作者
论文摘要
了解模拟与现实之间的差距对于使用腿部机器人进行强化学习至关重要，腿部机器人在模拟中受到了很大的训练。但是，最近的工作有时导致结论是哪些因素对成功很重要的因素，包括动态随机化的作用。在本文中，我们旨在清楚地了解动态随机化在学习laikago Quadrup的机器人的强大运动策略中的作用。令人惊讶的是，与使用相同机器人模型的先前工作相反，我们发现没有动态随机化或机器人自动自适应方案的直接SIM到现实传输是可能的。我们在SIM-TO-SIM设置中进行广泛的消融研究，以了解成功的政策转移基础的关键问题，包括其他可能影响政策鲁棒性的设计决策。我们通过以各种步态，速度和步进频率进行的SIM到真实实验进一步以结论为基础。其他详细信息：https：//www.pair.toronto.edu/understanding-dr/。
Understanding the gap between simulation and reality is critical for reinforcement learning with legged robots, which are largely trained in simulation. However, recent work has resulted in sometimes conflicting conclusions with regard to which factors are important for success, including the role of dynamics randomization. In this paper, we aim to provide clarity and understanding on the role of dynamics randomization in learning robust locomotion policies for the Laikago quadruped robot. Surprisingly, in contrast to prior work with the same robot model, we find that direct sim-to-real transfer is possible without dynamics randomization or on-robot adaptation schemes. We conduct extensive ablation studies in a sim-to-sim setting to understand the key issues underlying successful policy transfer, including other design decisions that can impact policy robustness. We further ground our conclusions via sim-to-real experiments with various gaits, speeds, and stepping frequencies. Additional Details: https://www.pair.toronto.edu/understanding-dr/.
