论文标题
动力学和域随机步态调制，曲线更为曲线，用于SIM到真实的腿部运动
Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion
论文作者
论文摘要
我们提出了一个SIM到现实的框架，该框架使用动态和域随机离线增强学习来增强腿部机器人的开环步态，从而使它们能够穿越不平坦的地形而不会感知脚部撞击。我们的方法是D $^2 $ - 随机的步态调制，其曲线（d $^2 $ -GMBC）使用增强随机搜索，并在模拟中训练随机动力学和地形，该策略修改了Quadrupedal Robote的开放环曲线GAIT GAIT生成器的参数和输出。该策略仅使用惯性测量值，即使机器人的物理参数与开环模型不匹配，机器人也能够穿越未知的粗糙地形。 我们将最终的策略与手工调整的曲线步态进行了比较，并在模拟和实际四足机器人的情况下与未随机进行训练的政策进行了比较。在d $^2 $ -GMBC的情况下，对未观察到的不均匀地形进行了多种实验，机器人的行走要比手工调整的步态或步态远得多，而没有域随机化。此外，使用D $^2 $ -GMBC，机器人可以在粗糙的地形上横向行走并旋转，即使它仅接受前进的训练。
We present a sim-to-real framework that uses dynamics and domain randomized offline reinforcement learning to enhance open-loop gaits for legged robots, allowing them to traverse uneven terrain without sensing foot impacts. Our approach, D$^2$-Randomized Gait Modulation with Bezier Curves (D$^2$-GMBC), uses augmented random search with randomized dynamics and terrain to train, in simulation, a policy that modifies the parameters and output of an open-loop Bezier curve gait generator for quadrupedal robots. The policy, using only inertial measurements, enables the robot to traverse unknown rough terrain, even when the robot's physical parameters do not match the open-loop model. We compare the resulting policy to hand-tuned Bezier Curve gaits and to policies trained without randomization, both in simulation and on a real quadrupedal robot. With D$^2$-GMBC, across a variety of experiments on unobserved and unknown uneven terrain, the robot walks significantly farther than with either hand-tuned gaits or gaits learned without domain randomization. Additionally, using D$^2$-GMBC, the robot can walk laterally and rotate while on the rough terrain, even though it was trained only for forward walking.
