# Quantifying the Reality Gap in Robotic Manipulation Tasks

arXiv: https://arxiv.org/abs/1811.01484 · HTML: https://mlsanity.com/paper/1811.01484/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [Interventional Causal Representation Learning](https://mlsanity.com/paper/2209.11924/) (2022)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges](https://mlsanity.com/paper/2311.18044/) (2023)
- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets](https://mlsanity.com/paper/2310.04517/) (2023)
- [ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction](https://mlsanity.com/paper/2210.06887/) (2022)
- [Generator Identification for Linear SDEs with Additive and Multiplicative Noise](https://mlsanity.com/paper/2310.19491/) (2023)
- [Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research](https://mlsanity.com/paper/2204.10297/) (2022)
- [Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine](https://mlsanity.com/paper/2209.06261/) (2022)
- [AugFL: Augmenting Federated Learning with Pretrained Models](https://mlsanity.com/paper/2503.02154/) (2025)
- [Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.19696/) (2025)
- [E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories](https://mlsanity.com/paper/2210.07887/) (2022)
- [Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems](https://mlsanity.com/paper/2406.17066/) (2024)
- [Control of Microrobots with Reinforcement Learning under On-Device Compute Constraints](https://mlsanity.com/paper/2512.24740/) (2025)
- [Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models](https://mlsanity.com/paper/2604.02438/) (2026)

## Abstract (arXiv)

We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world hardware without the need for physical access. The `reality gap' prevents solutions developed or learnt in simulation from performing well, or at at all, when transferred to real-world hardware. Making use of a Kinova robotic manipulator and a motion capture system, we record a ground truth enabling comparisons with various simulators, and present quantitative data for various manipulation-oriented robotic tasks. We show the relative strengths and weaknesses of numerous contemporary simulators, highlighting areas of significant discrepancy, and assisting researchers in the field in their selection of appropriate simulators for their use cases. All code and parameter listings are publicly available from: https://bitbucket.csiro.au/scm/~col549/quantifying-the-reality-gap-in-robotic-manipulation-tasks.git .