Bridging the Sim-to-Real Gap with Bayesian Inference

• URL: http://arxiv.org/abs/2403.16644v2
• Date: Sun, 1 Sep 2024 09:57:04 GMT
• Title: Bridging the Sim-to-Real Gap with Bayesian Inference
• Authors: Jonas Rothfuss, Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause,
• Abstract summary: We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
• Score: 53.61496586090384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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