# Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots

arXiv: https://arxiv.org/abs/2303.04136 · HTML: https://mlsanity.com/paper/2303.04136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience](https://mlsanity.com/paper/1810.05687/) (2018)
- [ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics](https://mlsanity.com/paper/1810.01054/) (2018)
- [BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators](https://mlsanity.com/paper/1906.01728/) (2019)
- [Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task](https://mlsanity.com/paper/1707.02267/) (2017)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?](https://mlsanity.com/paper/1903.11774/) (2019)
- [Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges](https://mlsanity.com/paper/2110.01358/) (2021)
- [DROPO: Sim-to-Real Transfer with Offline Domain Randomization](https://mlsanity.com/paper/2201.08434/) (2022)
- [Elastica: A compliant mechanics environment for soft robotic control](https://mlsanity.com/paper/2009.08422/) (2020)
- [DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration](https://mlsanity.com/paper/2102.11003/) (2021)
- [Sim-to-Real for Soft Robots using Differentiable FEM: Recipes for Meshing, Damping, and Actuation](https://mlsanity.com/paper/2201.12560/) (2022)
- [Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer](https://mlsanity.com/paper/2202.04575/) (2022)
- [A MATLAB Toolbox for Hybrid Rigid Soft Robots Based on the Geometric Variable Strain Approach](https://mlsanity.com/paper/2107.05494/) (2021)
- [Online vs. Offline Adaptive Domain Randomization Benchmark](https://mlsanity.com/paper/2206.14661/) (2022)

## Built on by (most cited first, 2 of 2)

- [Neural Fidelity Calibration for Informative Sim-to-Real Adaptation](https://mlsanity.com/paper/2504.08604/) (2025)
- [Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach](https://mlsanity.com/paper/2509.00319/) (2025)

## Abstract (arXiv)

Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability. However, the potentially infinite number of Degrees of Freedom makes their modeling a daunting task, and in many cases only an approximated description is available. This challenge makes reinforcement learning (RL) based approaches inefficient when deployed on a realistic scenario, due to the large domain gap between models and the real platform. In this work, we demonstrate, for the first time, how Domain Randomization (DR) can solve this problem by enhancing RL policies for soft robots with: i) robustness w.r.t. unknown dynamics parameters; ii) reduced training times by exploiting drastically simpler dynamic models for learning; iii) better environment exploration, which can lead to exploitation of environmental constraints for optimal performance. Moreover, we introduce a novel algorithmic extension to previous adaptive domain randomization methods for the automatic inference of dynamics parameters for deformable objects. We provide an extensive evaluation in simulation on four different tasks and two soft robot designs, opening interesting perspectives for future research on Reinforcement Learning for closed-loop soft robot control.