We present SIM-FSVGD for learning robot dynamics from data. As opposed to
traditional methods, SIM-FSVGD leverages low-fidelity physical priors, e.g., in
the form of simulators, to regularize the training of neural network models.
While learning accurate dynamics already in the low data regime, SIM-FSVGD
scales and excels also when more data is available. We empirically show that
learning with implicit physical priors results in accurate mean model
estimation as well as precise uncertainty quantification. We demonstrate the
effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a
high-performance RC racecar system. Using model-based RL, we demonstrate a
highly dynamic parking maneuver with drifting, using less than half the data
compared to the state of the art.

利用 SIM-FSVGD 学习机器人动力学，通过 simulators 对神经网络模型的训练进行正则化，实现准确的平均模型估计和精确的不确定性量化。在高性能 RC 赛车系统上实验表明，SIM-FSVGD 在缩小仿真与实际之间的差距和使用更少数据的情况下，比现有技术展示了高度动态的倒车和漂移停车技巧。