# Elastica: A compliant mechanics environment for soft robotic control

arXiv: https://arxiv.org/abs/2009.08422 · HTML: https://mlsanity.com/paper/2009.08422/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

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

- [Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning](https://mlsanity.com/paper/2307.16062/) (2023)
- [Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots](https://mlsanity.com/paper/2303.04136/) (2023)
- [Learning Soft Robotic Dynamics with Active Exploration](https://mlsanity.com/paper/2510.27428/) (2025)
- [Knowledge-based Neural Ordinary Differential Equations for Cosserat Rod-based Soft Robots](https://mlsanity.com/paper/2408.07776/) (2024)
- [Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots](https://mlsanity.com/paper/2504.13582/) (2025)
- [HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments](https://mlsanity.com/paper/2603.21421/) (2026)
- [ManiSoft: Towards Vision-Language Manipulation for Soft Continuum Robotics](https://mlsanity.com/paper/2605.18617/) (2026)
- [Hitting the Gym: Reinforcement Learning Control of Exercise-Strengthened Biohybrid Robots in Simulation](https://mlsanity.com/paper/2408.16069/) (2024)
- [Revealing Microscopic Objects in Fluorescence Live Imaging by Video-to-video Translation Based on A Spatial-temporal Generative Adversarial Network](https://mlsanity.com/paper/2502.16342/) (2025)
- [Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach](https://mlsanity.com/paper/2509.00319/) (2025)
- [Zero-shot Whole-Body Manipulation with a Large-Scale Soft Robotic Torso via Guided Reinforcement Learning](https://mlsanity.com/paper/2509.23556/) (2025)
- [A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms](https://mlsanity.com/paper/2511.02192/) (2025)
- [Rapidly Learning Soft Robot Control via Implicit Time-Stepping](https://mlsanity.com/paper/2511.06667/) (2025)
- [OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing](https://mlsanity.com/paper/2604.07423/) (2026)
- [Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations](https://mlsanity.com/paper/2606.08104/) (2026)
- [SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models](https://mlsanity.com/paper/2608.06650/) (2026)
- [WireSeg-32K: A Physics-Grounded Synthetic Dataset for Wire Instance Segmentation](https://mlsanity.com/paper/2609.03102/) (2026)

## Abstract (arXiv)

Soft robots are notoriously hard to control. This is partly due to the scarcity of models able to capture their complex continuum mechanics, resulting in a lack of control methodologies that take full advantage of body compliance. Currently available simulation methods are either too computational demanding or overly simplistic in their physical assumptions, leading to a paucity of available simulation resources for developing such control schemes. To address this, we introduce Elastica, a free, open-source simulation environment for soft, slender rods that can bend, twist, shear and stretch. We demonstrate how Elastica can be coupled with five state-of-the-art reinforcement learning algorithms to successfully control a soft, compliant robotic arm and complete increasingly challenging tasks.