# How Should a Simulation-to-Reality Transfer Budget Be Spent?

arXiv: https://arxiv.org/abs/2606.22062 · HTML: https://mlsanity.com/paper/2606.22062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 15 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [Understanding Domain Randomization for Sim-to-real Transfer](https://mlsanity.com/paper/2110.03239/) (2021)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Auto-Tuned Sim-to-Real Transfer](https://mlsanity.com/paper/2104.07662/) (2021)
- [Data-efficient Domain Randomization with Bayesian Optimization](https://mlsanity.com/paper/2003.02471/) (2020)
- [Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation](https://mlsanity.com/paper/2207.10821/) (2022)
- [How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?](https://mlsanity.com/paper/1903.11774/) (2019)
- [Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics](https://mlsanity.com/paper/2008.06686/) (2020)
- [DROPO: Sim-to-Real Transfer with Offline Domain Randomization](https://mlsanity.com/paper/2201.08434/) (2022)
- [AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer](https://mlsanity.com/paper/2302.04903/) (2023)
- [Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning](https://mlsanity.com/paper/2505.14266/) (2025)

## Abstract (arXiv)

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.