# Domain Randomization for Active Pose Estimation

arXiv: https://arxiv.org/abs/1903.03953 · HTML: https://mlsanity.com/paper/1903.03953/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective](https://mlsanity.com/paper/2303.05344/) (2023)
- [Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild](https://mlsanity.com/paper/2403.14539/) (2024)
- [SO-NeRF: Active View Planning for NeRF using Surrogate Objectives](https://mlsanity.com/paper/2312.03266/) (2023)
- [Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior](https://mlsanity.com/paper/2402.15402/) (2024)
- [RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications](https://mlsanity.com/paper/2404.03962/) (2024)
- [Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization](https://mlsanity.com/paper/2103.11144/) (2021)
- [Vision-based Perimeter Defense via Multiview Pose Estimation](https://mlsanity.com/paper/2209.12136/) (2022)
- [Leveraging Systematic Knowledge of 2D Transformations](https://mlsanity.com/paper/2206.00893/) (2022)
- [A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2208.00808/) (2022)
- [Simple Noisy Environment Augmentation for Reinforcement Learning](https://mlsanity.com/paper/2305.02882/) (2023)
- [CNN-Based Camera Pose Estimation and Localisation of Scan Images for Aircraft Visual Inspection](https://mlsanity.com/paper/2511.18702/) (2025)

## Abstract (arXiv)

Accurate state estimation is a fundamental component of robotic control. In robotic manipulation tasks, as is our focus in this work, state estimation is essential for identifying the positions of objects in the scene, forming the basis of the manipulation plan. However, pose estimation typically requires expensive 3D cameras or additional instrumentation such as fiducial markers to perform accurately. Recently, Tobin et al.~introduced an approach to pose estimation based on domain randomization, where a neural network is trained to predict pose directly from a 2D image of the scene. The network is trained on computer-generated images with a high variation in textures and lighting, thereby generalizing to real-world images. In this work, we investigate how to improve the accuracy of domain randomization based pose estimation. Our main idea is that active perception -- moving the robot to get a better estimate of pose -- can be trained in simulation and transferred to real using domain randomization. In our approach, the robot trains in a domain-randomized simulation how to estimate pose from a sequence of images. We show that our approach can significantly improve the accuracy of standard pose estimation in several scenarios: when the robot holding an object moves, when reference objects are moved in the scene, or when the camera is moved around the object.