# Interactive Differentiable Simulation

arXiv: https://arxiv.org/abs/1905.10706 · HTML: https://mlsanity.com/paper/1905.10706/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 17 of 17)

- [PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics](https://mlsanity.com/paper/2104.03311/) (2021)
- [gradSim: Differentiable simulation for system identification and visuomotor control](https://mlsanity.com/paper/2104.02646/) (2021)
- [Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language](https://mlsanity.com/paper/2110.15358/) (2021)
- [An End-to-End Differentiable Framework for Contact-Aware Robot Design](https://mlsanity.com/paper/2107.07501/) (2021)
- [Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact](https://mlsanity.com/paper/2103.16021/) (2021)
- [Efficient Differentiable Simulation of Articulated Bodies](https://mlsanity.com/paper/2109.07719/) (2021)
- [Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2011.01734/) (2020)
- [DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics](https://mlsanity.com/paper/2304.03223/) (2023)
- [Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics](https://mlsanity.com/paper/2205.02835/) (2022)
- [Fine-grained differentiable physics: a yarn-level model for fabrics](https://mlsanity.com/paper/2202.00504/) (2022)
- [Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation](https://mlsanity.com/paper/2211.02201/) (2022)
- [DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting](https://mlsanity.com/paper/2203.10263/) (2022)
- [Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine](https://mlsanity.com/paper/2209.06261/) (2022)
- [Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction](https://mlsanity.com/paper/2410.03920/) (2024)
- [A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data](https://mlsanity.com/paper/2203.00041/) (2022)
- [Complex Locomotion Skill Learning via Differentiable Physics](https://mlsanity.com/paper/2206.02341/) (2022)
- [Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions](https://mlsanity.com/paper/2503.11269/) (2025)

## Abstract (arXiv)

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables. We introduce Interactive Differentiable Simulation (IDS), a differentiable physics engine, that allows for efficient, accurate inference of physical properties of rigid-body systems. Integrated into deep learning architectures, our model is able to accomplish system identification using visual input, leading to an interpretable model of the world whose parameters have physical meaning. We present experiments showing automatic task-based robot design and parameter estimation for nonlinear dynamical systems by automatically calculating gradients in IDS. When integrated into an adaptive model-predictive control algorithm, our approach exhibits orders of magnitude improvements in sample efficiency over model-free reinforcement learning algorithms on challenging nonlinear control domains.