DROPO: Sim-to-Real Transfer with Offline Domain Randomization

• URL: http://arxiv.org/abs/2201.08434v1
• Date: Thu, 20 Jan 2022 20:03:35 GMT
• Title: DROPO: Sim-to-Real Transfer with Offline Domain Randomization
• Authors: Gabriele Tiboni and Karol Arndt and Ville Kyrki
• Abstract summary: We introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodelled phenomenon.
• Score: 12.778412161239466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, domain randomization has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodelled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods.

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