# Learning to Predict Robot Keypoints Using Artificially Generated Images

arXiv: https://arxiv.org/abs/1907.01879 · HTML: https://mlsanity.com/paper/1907.01879/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis](https://mlsanity.com/paper/2205.07058/) (2022)
- [NViSII: A Scriptable Tool for Photorealistic Image Generation](https://mlsanity.com/paper/2105.13962/) (2021)
- [Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments](https://mlsanity.com/paper/2401.09606/) (2024)
- [Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models](https://mlsanity.com/paper/2405.14877/) (2024)

## Abstract (arXiv)

This work considers robot keypoint estimation on color images as a supervised machine learning task. We propose the use of probabilistically created renderings to overcome the lack of labeled real images. Rather than sampling from stationary distributions, our approach introduces a feedback mechanism that constantly adapts probability distributions according to current training progress. Initial results show, our approach achieves near-human-level accuracy on real images. Additionally, we demonstrate that feedback leads to fewer required training steps, while maintaining the same model quality on synthetic data sets.