Self-Supervised Learning for Object Pose Estimation Through Active Real Sample Capture
Bibliographic record
Abstract
6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. In industrial bin-picking tasks, this problem becomes especially challenging due to difficult object poses, complex occlusions, and inter-object ambiguities. In this work, we propose a novel self-supervised method that automatically collects, labels, and fine-tunes on real images using an eye-in-hand camera setup. We leverage the mobile camera to first obtain reliable ground-truth estimates through multi-view pose estimation, allowing us to subsequently reposition the camera to capture and label real ‘hard case’ samples from the estimated scene. This process enables closure of the sim-to-real gap through large quantities of targeted real training data, generated by comparing differences in model performance between real and synthetically reconstructed scenes and informing the mobile camera on specific poses or areas for data capture. We surpass state-of-the-art performance on a challenging bin-picking benchmark: five out of seven objects surpass a 95% correct detection rate, compared to only one out of seven for previous methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".