# Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine

arXiv: https://arxiv.org/abs/2209.06261 · HTML: https://mlsanity.com/paper/2209.06261/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 60 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [A Differentiable Physics Engine for Deep Learning in Robotics](https://mlsanity.com/paper/1611.01652/) (2016)
- [gradSim: Differentiable simulation for system identification and visuomotor control](https://mlsanity.com/paper/2104.02646/) (2021)
- [BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators](https://mlsanity.com/paper/1906.01728/) (2019)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting](https://mlsanity.com/paper/2105.12244/) (2021)
- [CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation](https://mlsanity.com/paper/2109.09163/) (2021)
- [DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools](https://mlsanity.com/paper/2203.17275/) (2022)
- [Planar Robot Casting with Real2Sim2Real Self-Supervised Learning](https://mlsanity.com/paper/2111.04814/) (2021)
- [Differentiable Simulation of Soft Multi-body Systems](https://mlsanity.com/paper/2205.01758/) (2022)
- [Differentiable Molecular Simulations for Control and Learning](https://mlsanity.com/paper/2003.00868/) (2020)
- [Interactive Differentiable Simulation](https://mlsanity.com/paper/1905.10706/) (2019)
- [Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation](https://mlsanity.com/paper/2109.08815/) (2021)
- [Quantifying the Reality Gap in Robotic Manipulation Tasks](https://mlsanity.com/paper/1811.01484/) (2018)
- [Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics](https://mlsanity.com/paper/2205.02835/) (2022)
- [Deep Reinforcement Learning for Tensegrity Robot Locomotion](https://mlsanity.com/paper/1609.09049/) (2016)
- [A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines](https://mlsanity.com/paper/2004.13859/) (2020)
- [Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots](https://mlsanity.com/paper/2011.04929/) (2020)
- [A Differentiable Newton-Euler Algorithm for Real-World Robotics](https://mlsanity.com/paper/2110.12422/) (2021)
- [A Bio-Inspired Tensegrity Manipulator with Multi-DOF, Structurally Compliant Joints](https://mlsanity.com/paper/1604.08667/) (2016)
- [Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots](https://mlsanity.com/paper/1804.06527/) (2018)
- [GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients](https://mlsanity.com/paper/2109.06976/) (2021)
- [A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data](https://mlsanity.com/paper/2203.00041/) (2022)
- [Parameter Identification and Motion Control for Articulated Rigid Body Robots Using Differentiable Position-based Dynamics](https://mlsanity.com/paper/2201.05753/) (2022)

## Built on by (most cited first, 6 of 6)

- [Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models](https://mlsanity.com/paper/2310.18308/) (2023)
- [GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy](https://mlsanity.com/paper/2309.09051/) (2023)
- [Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space](https://mlsanity.com/paper/2505.17389/) (2025)
- [Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions](https://mlsanity.com/paper/2503.11269/) (2025)
- [Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters](https://mlsanity.com/paper/2508.13303/) (2025)
- [Morphology-Aware Graph Reinforcement Learning for Tensegrity Robot Locomotion](https://mlsanity.com/paper/2510.26067/) (2025)

## Abstract (arXiv)

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot.