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Closing the Sim2Real Performance Gap in RL

robotics › sim-to-real

📄 Abstract

Abstract: Sim2Real aims at training policies in high-fidelity simulation environments and effectively transferring them to the real world. Despite the developments of accurate simulators and Sim2Real RL approaches, the policies trained purely in simulation often suffer significant performance drops when deployed in real environments. This drop is referred to as the Sim2Real performance gap. Current Sim2Real RL methods optimize the simulator accuracy and variability as proxies for real-world performance. However, these metrics do not necessarily correlate with the real-world performance of the policy as established theoretically and empirically in the literature. We propose a novel framework to address this issue by directly adapting the simulator parameters based on real-world performance. We frame this problem as a bi-level RL framework: the inner-level RL trains a policy purely in simulation, and the outer-level RL adapts the simulation model and in-sim reward parameters to maximize real-world performance of the in-sim policy. We derive and validate in simple examples the mathematical tools needed to develop bi-level RL algorithms that close the Sim2Real performance gap.

Authors (5)

Akhil S Anand

Shambhuraj Sawant

Jasper Hoffmann

Dirk Reinhardt

Sebastien Gros

Submitted

October 20, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper proposes a novel framework to directly address the Sim2Real performance gap in Reinforcement Learning by adapting simulator parameters based on real-world performance. It frames this as a bi-level RL problem where an outer-level RL agent tunes the simulation model and reward parameters to maximize real-world policy performance, overcoming the limitation that current methods optimize indirect proxies.

Business Value

Enables more reliable and efficient development of robotic systems and autonomous agents by reducing the need for extensive real-world testing and fine-tuning.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

High, as it provides a principled method for improving the transferability of simulated policies.

Limitations Addressed

The significant performance drop (Sim2Real gap) when transferring RL policies from simulation to the real world, and the inadequacy of current methods that optimize simulator accuracy as a proxy for real-world performance.

Performance Gains

Directly optimizes for real-world performance, aiming to significantly reduce or eliminate the Sim2Real gap.

Technical Tags

Sim2Real TransferReinforcement Learning (RL)Performance GapSimulator AdaptationBi-level RLReal-world PerformancePolicy TransferSimulation Environments

Research Topics

RoboticsReinforcement LearningSim2Real TransferSimulation-to-RealityControl Theory

Methods & Architectures

Bi-level RL frameworkDirect simulator parameter adaptationReal-world performance feedback loop Reinforcement Learning policiesSimulator models

Applications & Tasks

Robotics Autonomous Systems Simulation-based Training Significant performance drops when transferring RL policies from simulation to the real world (Sim2Real gap)Current Sim2Real methods optimize proxies (simulator accuracy) that don't correlate with real-world policy performance Closing the Sim2Real performance gapTraining robust RL policies for real-world deploymentAdapting simulation environments to match reality

Related Fields

RoboticsReinforcement LearningSimulationControl SystemsMachine Learning

Keywords

Sim2RealReinforcement learningRoboticsSimulationPolicy transferPerformance gapSimulator adaptationBi-level optimizationReal-world deploymentControl

Academic Context

#Robotics#Reinforcement Learning#Sim2Real Transfer#Simulation-to-Reality#Control Theory

Technology Stack

Frameworks & Libraries

Reinforcement Learning libraries

Commercial Potential

Potential Products

Robotic control softwareSimulation platforms with adaptive capabilitiesTools for accelerating RL development

Target Industries

RoboticsAutomotiveAerospaceManufacturingLogistics

Use Case Examples

Training autonomous driving policies in simulation and deploying them safely in carsDeveloping robotic manipulation skills that transfer directly to physical robotsAccelerating the development cycle for new robotic applications

Competitive Edge

Offers a more direct and effective approach to closing the Sim2Real gap compared to methods relying solely on simulator fidelity or domain randomization.

Market Opportunity

Large and growing market for robotics and autonomous systems.

Revenue Models

Licensing of Sim2Real adaptation technologydevelopment services for robotic systems.

Resource Requirements

Compute Needs

High, requires significant computational resources for training the bi-level RL system.

Data Requirements

Requires access to a high-fidelity simulator and real-world data for adaptation.

Deployment Constraints

The adaptation process itself requires real-world interaction or data, which can be costly.

Scalability

The bi-level RL framework is designed to be scalable to complex robotic tasks.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years

Patent Potential

High, for the bi-level RL framework for simulator adaptation.

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