# Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation

arXiv: https://arxiv.org/abs/2207.10821 · HTML: https://mlsanity.com/paper/2207.10821/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 30 of 30)

- [Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots](https://mlsanity.com/paper/2310.13724/) (2023)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation](https://mlsanity.com/paper/2306.11290/) (2023)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [Retrospectives on the Embodied AI Workshop](https://mlsanity.com/paper/2210.06849/) (2022)
- [CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos](https://mlsanity.com/paper/2411.17820/) (2024)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Phone2Proc: Bringing Robust Robots Into Our Chaotic World](https://mlsanity.com/paper/2212.04819/) (2022)
- [ViNL: Visual Navigation and Locomotion Over Obstacles](https://mlsanity.com/paper/2210.14791/) (2022)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin](https://mlsanity.com/paper/2211.14874/) (2022)
- [Exploitation-Guided Exploration for Semantic Embodied Navigation](https://mlsanity.com/paper/2311.03357/) (2023)
- [One Filter to Deploy Them All: Robust Safety for Quadrupedal Navigation in Unknown Environments](https://mlsanity.com/paper/2412.09989/) (2024)
- [A Closer Look at Reward Decomposition for High-Level Robotic Explanations](https://mlsanity.com/paper/2304.12958/) (2023)
- [Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer](https://mlsanity.com/paper/2412.09417/) (2024)
- [Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction](https://mlsanity.com/paper/2306.03857/) (2023)
- [Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second](https://mlsanity.com/paper/2306.07552/) (2023)
- [Learning to navigate efficiently and precisely in real environments](https://mlsanity.com/paper/2401.14349/) (2024)
- [A General Purpose Supervisory Signal for Embodied Agents](https://mlsanity.com/paper/2212.01186/) (2022)
- [From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy](https://mlsanity.com/paper/2503.11007/) (2025)
- [Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip](https://mlsanity.com/paper/2307.15931/) (2023)
- [Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation](https://mlsanity.com/paper/2503.05092/) (2025)
- [Synthetic vs. Real Training Data for Visual Navigation](https://mlsanity.com/paper/2509.11791/) (2025)
- [Abstract Sim2Real through Approximate Information States](https://mlsanity.com/paper/2604.15289/) (2026)
- [FlashNav: Training Deployable Robot Navigation Policies in Seconds](https://mlsanity.com/paper/2606.15846/) (2026)
- [How Should a Simulation-to-Reality Transfer Budget Be Spent?](https://mlsanity.com/paper/2606.22062/) (2026)
- [SimVerity: When Does Simulated Agent Success Survive Physical Deployment?](https://mlsanity.com/paper/2608.25067/) (2026)
- [RTNav: Towards Real-Time Zero-Shot Object Navigation](https://mlsanity.com/paper/2608.26496/) (2026)

## Abstract (arXiv)

If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simulators of increasing fidelity (since reality is what it is). We challenge this assumption and present a contrary hypothesis -- sim2real transfer of robots may be improved with lower (not higher) fidelity simulation. We conduct a systematic large-scale evaluation of this hypothesis on the problem of visual navigation -- in the real world, and on 2 different simulators (Habitat and iGibson) using 3 different robots (A1, AlienGo, Spot). Our results show that, contrary to expectation, adding fidelity does not help with learning; performance is poor due to slow simulation speed (preventing large-scale learning) and overfitting to inaccuracies in simulation physics. Instead, building simple models of the robot motion using real-world data can improve learning and generalization.