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

• URL: http://arxiv.org/abs/2606.22062v2
• Date: Thu, 25 Jun 2026 14:18:48 GMT
• Title: How Should a Simulation-to-Reality Transfer Budget Be Spent?
• Abstract summary: We focus on the allocation of real-robot measurement time between system identification and domain randomization.<n>Across the reality gaps and noise levels we tested, the measurement budget did most of the work.<n>Overall, our results suggest that sim-to-real pipelines should first measure the parameters they can and reserve randomization for the uncertainty that remains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Learning Shortest Paths When Data is Scarce [3.3012620893449465]
  We study a shortest-path problem in which a planner has access to abundant synthetic samples, limited real-world observations, and an edge-similarity capturing expected behavioral similarity across links.<n>We model the simulator-to-reality discrepancy as an unknown, edge-specific bias that varies smoothly over the similarity graph, and estimate it using Laplacian-regularized least squares.<n>For cold-start settings without initial real data, we develop a bias-aware active learning algorithm that adaptively selects edges to measure until a prescribed accuracy is met.
  arXiv  Detail & Related papers  (2026-01-07T06:19:04Z)
• G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration [48.948187359727996]
  G-Sim is a hybrid framework that automates simulator construction with rigorous empirical calibration.<n>It produces reliable, causally-informed simulators, mitigating data-inefficiency and enabling robust system-level interventions.
  arXiv  Detail & Related papers  (2025-06-10T22:14:34Z)
• Accelerated zero-order SGD under high-order smoothness and overparameterized regime [79.85163929026146]
  We present a novel gradient-free algorithm to solve convex optimization problems. Such problems are encountered in medicine, physics, and machine learning. We provide convergence guarantees for the proposed algorithm under both types of noise.
  arXiv  Detail & Related papers  (2024-11-21T10:26:17Z)
• Robust Visual Sim-to-Real Transfer for Robotic Manipulation [79.66851068682779]
  Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR)
  arXiv  Detail & Related papers  (2023-07-28T05:47:24Z)
• Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks [2.018504891256636]
  We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation.
  arXiv  Detail & Related papers  (2022-06-13T16:12:56Z)
• Robot Learning from Randomized Simulations: A Review [59.992761565399185]
  Deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. State-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive. We focus on a technique named 'domain randomization' which is a method for learning from randomized simulations.
  arXiv  Detail & Related papers  (2021-11-01T13:55:41Z)
• Understanding Domain Randomization for Sim-to-real Transfer [41.33483293243257]
  We propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters. We prove that sim-to-real transfer can succeed under mild conditions without any real-world training samples.
  arXiv  Detail & Related papers  (2021-10-07T07:45:59Z)
• Simulation-based Bayesian inference for multi-fingered robotic grasping [6.677646909984405]
  Multi-fingered robotic grasping is an undeniable stepping stone to universal picking and dexterous manipulation. Yet, multi-fingered grippers remain challenging to control because of their rich nonsmooth contact dynamics or because of noise. We propose a novel simulation-based approach for full Bayesian inference based on a deep neural network surrogate of the likelihood-to-evidence ratio.
  arXiv  Detail & Related papers  (2021-09-29T08:44:36Z)
• Auto-Tuned Sim-to-Real Transfer [143.44593793640814]
  Policies trained in simulation often fail when transferred to the real world. Current approaches to tackle this problem, such as domain randomization, require prior knowledge and engineering. We propose a method for automatically tuning simulator system parameters to match the real world.
  arXiv  Detail & Related papers  (2021-04-15T17:59:55Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.