# Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network

arXiv: https://arxiv.org/abs/2508.14100 · HTML: https://mlsanity.com/paper/2508.14100/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Soft Robots Modeling: a Structured Overview](https://mlsanity.com/paper/2112.03645/) (2021)
- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/1905.11173/) (2019)
- [Regularized Cycle Consistent Generative Adversarial Network for Anomaly Detection](https://mlsanity.com/paper/2001.06591/) (2020)
- [A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN](https://mlsanity.com/paper/2310.03759/) (2023)
- [Adaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning](https://mlsanity.com/paper/2503.16540/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Continual Learning Framework for Adaptive Control of Modular Soft Robots](https://mlsanity.com/paper/2607.06740/) (2026)

## Abstract (arXiv)

Deep learning provides a powerful method for modeling the dynamics of soft robots, offering advantages over traditional analytical approaches that require precise knowledge of the robot's structure, material properties, and other physical characteristics. Given the inherent complexity and non-linearity of these systems, extracting such details can be challenging. The mappings learned in one domain cannot be directly transferred to another domain with different physical properties. This challenge is particularly relevant for soft robots, as their materials gradually degrade over time. In this paper, we introduce a domain translation framework based on a conditional cycle generative adversarial network (CCGAN) to enable knowledge transfer from a source domain to a target domain. Specifically, we employ a dynamic learning approach to adapt a pose controller trained in a standard simulation environment to a domain with tenfold increased viscosity. Our model learns from input pressure signals conditioned on corresponding end-effector positions and orientations in both domains. We evaluate our approach through trajectory-tracking experiments across five distinct shapes and further assess its robustness under noise perturbations and periodicity tests. The results demonstrate that CCGAN-GP effectively facilitates cross-domain skill transfer, paving the way for more adaptable and generalizable soft robotic controllers.