# Sim-to-Real Transfer of Robotic Assembly with Visual Inputs Using CycleGAN and Force Control

arXiv: https://arxiv.org/abs/2208.14104 · HTML: https://mlsanity.com/paper/2208.14104/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation](https://mlsanity.com/paper/2407.12449/) (2024)
- [Stable-Sim2Real: Exploring Simulation of Real-Captured 3D Data with Two-Stage Depth Diffusion](https://mlsanity.com/paper/2507.23483/) (2025)
- [Multimodal Fusion for Sim2real Transfer in Visual Reinforcement Learning](https://mlsanity.com/paper/2507.09180/) (2025)
- [Sim-to-Real Transfer via a Style-Identified Cycle Consistent Generative Adversarial Network: Zero-Shot Deployment on Robotic Manipulators through Visual Domain Adaptation](https://mlsanity.com/paper/2601.16677/) (2026)
- [Reinforcement Learning from Cross-domain Videos with Video Prediction Model](https://mlsanity.com/paper/2606.03201/) (2026)

## Abstract (arXiv)

Recently, deep reinforcement learning (RL) has shown some impressive successes in robotic manipulation applications. However, training robots in the real world is nontrivial owing to sample efficiency and safety concerns. Sim-to-real transfer is proposed to address the aforementioned concerns but introduces a new issue called the reality gap. In this work, we introduce a sim-to-real learning framework for vision-based assembly tasks and perform training in a simulated environment by employing inputs from a single camera to address the aforementioned issues. We present a domain adaptation method based on cycle-consistent generative adversarial networks (CycleGAN) and a force control transfer approach to bridge the reality gap. We demonstrate that the proposed framework trained in a simulated environment can be successfully transferred to a real peg-in-hole setup.