# Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation

arXiv: https://arxiv.org/abs/2002.02538 · HTML: https://mlsanity.com/paper/2002.02538/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [AKB-48: A Real-World Articulated Object Knowledge Base](https://mlsanity.com/paper/2202.08432/) (2022)
- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [Auto-Tuned Sim-to-Real Transfer](https://mlsanity.com/paper/2104.07662/) (2021)
- [NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance](https://mlsanity.com/paper/2505.08712/) (2025)
- [DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects](https://mlsanity.com/paper/2204.03139/) (2022)
- [X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real](https://mlsanity.com/paper/2505.07096/) (2025)
- [A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation](https://mlsanity.com/paper/2112.05068/) (2021)
- [Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization](https://mlsanity.com/paper/2207.14561/) (2022)
- [SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing](https://mlsanity.com/paper/2404.02569/) (2024)
- [A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards](https://mlsanity.com/paper/2502.08643/) (2025)
- [Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning](https://mlsanity.com/paper/2403.08152/) (2024)
- [Learning to Play Piano in the Real World](https://mlsanity.com/paper/2503.15481/) (2025)
- [RoboPearls: Editable Video Simulation for Robot Manipulation](https://mlsanity.com/paper/2506.22756/) (2025)
- [Enhancing Sim2Real Transfer for Torque-Controlled Robots through Real2Sim Dynamics Estimation and Reinforcement Learning](https://mlsanity.com/paper/2608.22629/) (2026)

## Abstract (arXiv)

This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task. This strategy consists of three steps: (1) using the rough environment with the estimated models to develop the methods to complete the manipulation task in the simulation; (2) applying the methods from simulation to real-world and comparing their performance; (3) updating the models and methods in simulation based on the differences between the real world and the simulation. The Plug Task from the 2015 DARPA Robotics Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new identification approach for building the model of the linear flexible objects is derived from real-world to simulation. The automation of the DRC plug task in both simulation and real-world proves the success of the Sim2Real2Sim strategy. Numerical experiments are implemented to validate the simulated model.