Bridging the Sim-to-Real Gap with Bayesian Inference
Jonas Rothfuss Bhavya Sukhija Lenart Treven Florian Dörfler Stelian Coros Andreas Krause
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.