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Picking Bins Empty: A Hierarchical Hybrid Approach with Online Self-Learning of Grasp Points for Reliable Industrial Bin-Picking — arXiv:2608.28175

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28175
License (as archived): CC0-1.0
Archived: 2026-10-17T11:51:34.264Z
SHA-256 of the archived text: 837c108559b9257e9e1c7a1898a7ffb1c44c6ef8aad70fc9a26feeda70947122
Stable URL of this page: https://metraipolis.com/documento/dc_97ed419eb484

How to cite

Picking Bins Empty: A Hierarchical Hybrid Approach with Online Self-Learning of Grasp Points for Reliable Industrial Bin-Picking — arXiv:2608.28175 — arXiv (en), https://arxiv.org/abs/2608.28175 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_97ed419eb484 (Metraipolis archive dc_97ed419eb484, sha256 837c108559b9257e9e1c7a1898a7ffb1c44c6ef8aad70fc9a26feeda70947122)

BibTeX

@misc{dc97ed419eb484,
  title = {Picking Bins Empty: A Hierarchical Hybrid Approach with Online Self-Learning of Grasp Points for Reliable Industrial Bin-Picking — arXiv:2608.28175},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28175},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_97ed419eb484; sha256 837c108559b9257e9e1c7a1898a7ffb1c44c6ef8aad70fc9a26feeda70947122},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28175. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Bin-picking is a cornerstone of modern manufacturing, yet achieving complete bin clearance without manual intervention remains a critical challenge. While model-based methods provide high precision, they frequently suffer from deadlocks when predefined grasps are occluded or perception fails. Labor-intensive fine-tuning of grasp points is commonly required to reach a satisfactory performance for new parts. Model-free algorithms offer a more flexible alternative with "out-of-the-box" versatility but lack the reliability and repeatability required for production. Unlike existing work, which treats the two techniques in isolation, we propose a fourtiered hierarchical hybrid approach to combine the best of both worlds. A model-based pipeline serves as a robust backbone, while a model-free "exploration agent" resolves deadlock situations and discovers new grasp points. This is supported by an online self-learning mechanism that uses gripper-stroke feedback and Wilson score intervals to autonomously rank grasp candidates, reducing manual commissioning effort. Validation on three automotive parts demonstrates that our method significantly outperforms a model-free baseline in grasp success rate while improving the bin clearance rate of the model-based baseline from 50.9% to 100% across all experiments. This transition to full bin clearance marks a significant step towards truly autonomous, intervention-free industrial operation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-31 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2608.28175 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T11:18:08.715Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).