Learning to Predict Robot Keypoints Using Artificially Generated Images
This work addresses the problem of data scarcity in robot vision for researchers and practitioners, though it appears incremental as it builds on existing synthetic data methods with a feedback mechanism.
The paper tackles robot keypoint estimation by using probabilistically generated synthetic images to address the lack of labeled real data, achieving near-human-level accuracy on real images and reducing training steps while maintaining model quality on synthetic data.
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.