# The Reality Gap in Robotics: Challenges, Solutions, and Best Practices

arXiv: https://arxiv.org/abs/2510.20808 · HTML: https://mlsanity.com/paper/2510.20808/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 123 of its 166 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 30 of 123)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Evaluating Real-World Robot Manipulation Policies in Simulation](https://mlsanity.com/paper/2405.05941/) (2024)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://mlsanity.com/paper/2406.02523/) (2024)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning](https://mlsanity.com/paper/1803.11347/) (2018)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)

## Built on by (most cited first, 24 of 24)

- [From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model](https://mlsanity.com/paper/2605.22671/) (2026)
- [House of Dextra: Cross-embodied Co-design for Dexterous Hands](https://mlsanity.com/paper/2512.03743/) (2025)
- [Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators](https://mlsanity.com/paper/2607.07196/) (2026)
- [A testable framework for AI alignment: Simulation Theology as an engineered worldview for silicon-based agents](https://mlsanity.com/paper/2602.16987/) (2026)
- [Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments](https://mlsanity.com/paper/2603.07578/) (2026)
- [Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration](https://mlsanity.com/paper/2603.22273/) (2026)
- [Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2510.26656/) (2025)
- [RAPID: Reconfigurable, Adaptive Platform for Iterative Design](https://mlsanity.com/paper/2602.06653/) (2026)
- [aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones](https://mlsanity.com/paper/2602.07264/) (2026)
- [Deepmechanics](https://mlsanity.com/paper/2602.18060/) (2026)
- [When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited](https://mlsanity.com/paper/2605.17017/) (2026)
- [WorldString: Actionable World Representation](https://mlsanity.com/paper/2605.18743/) (2026)
- [Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment](https://mlsanity.com/paper/2605.27659/) (2026)
- [Bridging Performance and Generalization in Reinforcement Learning for Agile Flight](https://mlsanity.com/paper/2606.27348/) (2026)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)
- [Real-Time Source-Free Object Detection](https://mlsanity.com/paper/2606.31834/) (2026)
- [Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning](https://mlsanity.com/paper/2607.03168/) (2026)
- [Towards Illusions Awareness in Cyber-Physical System's Design](https://mlsanity.com/paper/2609.17260/) (2026)
- [A Sim-to-Real Integration Pipeline for Training and Deployment of Chunk-Based VLA Manipulation Policies](https://mlsanity.com/paper/2609.21817/) (2026)
- [A Deployment Study of Identity-Gated Drone Gesture Control](https://mlsanity.com/paper/2609.25511/) (2026)
- [PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning](https://mlsanity.com/paper/2609.25630/) (2026)
- [Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage](https://mlsanity.com/paper/2609.30214/) (2026)
- [Robot-GST: geometry-aware spatial-temporal robot policy representation and evaluation](https://mlsanity.com/paper/2609.33872/) (2026)
- [Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap](https://mlsanity.com/paper/2610.00030/) (2026)

## Abstract (arXiv)

Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driven by the extensive use of simulation as a critical tool for training and testing robotic systems prior to their deployment in real-world environments. However, simulations consist of abstractions and approximations that inevitably introduce discrepancies between simulated and real environments, known as the reality gap. These discrepancies significantly hinder the successful transfer of systems from simulation to the real world. Closing this gap remains one of the most pressing challenges in robotics. Recent advances in sim-to-real transfer have demonstrated promising results across various platforms, including locomotion, navigation, and manipulation. By leveraging techniques such as domain randomization, real-to-sim transfer, state and action abstractions, and sim-real co-training, many works have overcome the reality gap. However, challenges persist, and a deeper understanding of the reality gap's root causes and solutions is necessary. In this survey, we present a comprehensive overview of the sim-to-real landscape, highlighting the causes, solutions, and evaluation metrics for the reality gap and sim-to-real transfer.