🤖 AI Summary
This study addresses the optimal allocation of limited real-world measurement time between system identification and domain randomization to enhance sim-to-real transfer performance in robot learning. Through controlled simulation-to-simulation experiments on a pendulum system, the work presents the first quantitative analysis of the trade-off between parameter identification accuracy and the breadth of domain randomization under a fixed real-data budget. The results demonstrate that, under identifiable dynamics, even a small amount of real data used for precise system identification significantly reduces the reality gap. In contrast, broad domain randomization—even when encompassing the true system parameters—fails to match the effectiveness of accurate parameter estimation. These findings reveal a key strategy for efficiently leveraging scarce real-world data and establish a new paradigm for improving sim-to-real transfer.
📝 Abstract
Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynamics is still poorly understood. In this work, we focused on the allocation of real-robot measurement time between system identification and domain randomization. We studied this tradeoff in a controlled sim-to-sim pendulum setting, where a hidden-parameter model stands in for the physical robot, and the experiment sweeps identification rollouts against the width of the randomization distribution. Across the reality gaps and noise levels we tested, the measurement budget did most of the work. A small number of identification rollouts closed most of the transfer gap, and once any real data was available, policies performed best when trained at the estimated parameters rather than over a widened randomization band. Broad randomization that contained the true system still did not substitute for measurement. These results hold in a benign regime where the dynamics are identifiable and only two parameters are unknown, so structural model mismatch remains the setting where randomization breadth may become more valuable. Overall, our results suggest that sim-to-real pipelines should first measure the parameters they can and reserve randomization for the uncertainty that remains.