Training deep reinforcement learning (DRL) locomotion policies often require
massive amounts of data to converge to the desired behaviour. In this regard,
simulators provide a cheap and abundant source. For successful sim-to-real
transfer, exhaustively engineered approaches such as system identification,
dynamics randomization, and domain adaptation are generally employed. As an
alternative, we investigate a simple strategy of random force injection (RFI)
to perturb system dynamics during training. We show that the application of
random forces enables us to emulate dynamics randomization. This allows us to
obtain locomotion policies that are robust to variations in system dynamics. We
further extend RFI, referred to as extended random force injection (ERFI), by
introducing an episodic actuation offset. We demonstrate that ERFI provides
additional robustness for variations in system mass offering on average a 53%
improved performance over RFI. We also show that ERFI is sufficient to perform
a successful sim-to-real transfer on two different quadrupedal platforms,
ANYmal C and Unitree A1, even for perceptive locomotion over uneven terrain in
outdoor environments.

通过引入随机力注入的策略，该研究为解决深度增强学习中数据收敛需求高问题提供了简单有效的方案，并成功将该方法用于四足机器人模拟模型到真实模型的转移。