# Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification

arXiv: https://arxiv.org/abs/2409.11512 · HTML: https://mlsanity.com/paper/2409.11512/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets](https://mlsanity.com/paper/2109.13396/) (2021)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [Self-supervised 6D Object Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/1909.10159/) (2019)
- [Self6D: Self-Supervised Monocular 6D Object Pose Estimation](https://mlsanity.com/paper/2004.06468/) (2020)
- [BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects](https://mlsanity.com/paper/2403.09799/) (2024)
- [Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation](https://mlsanity.com/paper/2203.10339/) (2022)
- [Self-supervised Learning for Precise Pick-and-place without Object Model](https://mlsanity.com/paper/2006.08373/) (2020)
- [A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation](https://mlsanity.com/paper/1703.03347/) (2017)
- [Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter](https://mlsanity.com/paper/1904.07402/) (2019)
- [KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matching](https://mlsanity.com/paper/2303.16102/) (2023)
- [Improving Data Efficiency of Self-supervised Learning for Robotic Grasping](https://mlsanity.com/paper/1903.00228/) (2019)
- [ParaPose: Parameter and Domain Randomization Optimization for Pose Estimation using Synthetic Data](https://mlsanity.com/paper/2203.00945/) (2022)
- [In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components](https://mlsanity.com/paper/2208.01284/) (2022)
- [Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task](https://mlsanity.com/paper/2309.16221/) (2023)
- [Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach](https://mlsanity.com/paper/2003.03717/) (2020)

## Built on by (most cited first, 2 of 2)

- [Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds](https://mlsanity.com/paper/2512.07211/) (2025)
- [Towards a Modular Bin-picking Framework for Handling Object Pose Uncertainties](https://mlsanity.com/paper/2607.13698/) (2026)

## Abstract (arXiv)

In this paper, we present a novel method for self-supervised fine-tuning of pose estimation. Leveraging zero-shot pose estimation, our approach enables the robot to automatically obtain training data without manual labeling. After pose estimation the object is grasped, and in-hand pose estimation is used for data validation. Our pipeline allows the system to fine-tune while the process is running, removing the need for a learning phase. The motivation behind our work lies in the need for rapid setup of pose estimation solutions. Specifically, we address the challenging task of bin picking, which plays a pivotal role in flexible robotic setups. Our method is implemented on a robotics work-cell, and tested with four different objects. For all objects, our method increases the performance and outperforms a state-of-the-art method trained on the CAD model of the objects. Project page available at gogoengine.github.io