Metraipolis › archive › dc_ffedfdf47eb4

Plan-Conditioned Imitation for Robust Object Retrieval under Self-Occlusion in Dense Clutter — arXiv:2609.38857

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38857
License (as archived): CC0-1.0
Archived: 2026-10-07T15:11:43.402Z
SHA-256 of the archived text: 2c59c06b508728117389dc12f9d7d42a2515c8673dd9dfa89285cffd41d783e4
Stable URL of this page: https://metraipolis.com/documento/dc_ffedfdf47eb4

How to cite

Plan-Conditioned Imitation for Robust Object Retrieval under Self-Occlusion in Dense Clutter — arXiv:2609.38857 — arXiv (en), https://arxiv.org/abs/2609.38857 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ffedfdf47eb4 (Metraipolis archive dc_ffedfdf47eb4, sha256 2c59c06b508728117389dc12f9d7d42a2515c8673dd9dfa89285cffd41d783e4)

BibTeX

@misc{dcffedfdf47eb4,
  title = {Plan-Conditioned Imitation for Robust Object Retrieval under Self-Occlusion in Dense Clutter — arXiv:2609.38857},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38857},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ffedfdf47eb4; sha256 2c59c06b508728117389dc12f9d7d42a2515c8673dd9dfa89285cffd41d783e4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38857. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Retrieving objects from dense clutter requires rearrangement during which the manipulator can occlude objects while moving them. Repeated arm withdrawals to restore visibility interrupt execution. We introduce TRACE, a plan-conditioned imitation framework for retrieval under self-occlusion. A single unoccluded observation initializes a digital twin, where a privileged teacher generates a fixed nominal rollout. A recurrent student combines local rollout context, partial object observations, and proprioception to select actions that can correct deviations from the prediction. Behavior cloning initializes the student; DAgger refines it with teacher labels on student-visited states. The rollout remains fixed throughout execution, so the deployed student needs neither online teacher queries nor additional simulator rollouts during pushing. On 511 simulation test scenes, TRACE achieves 90.7% success versus 43.4% for nominal replay and 96.7% for the privileged closed-loop teacher. At a matched 26,373-label budget, student-state supervision achieves 87.8% versus 66.7% for expert-only cloning, demonstrating gains beyond additional labels. On a UR5e, TRACE achieves 90.0% success versus 95.0% for the closed-loop teacher, while reducing total execution time from 192.7 s to 67.3 s. It avoids the teacher's 16.8 sensing-related arm retractions per trial during pushing, retaining a final withdrawal for graspability evaluation. Code and data will be released at: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-01 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.38857 — 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 2026-10-07T14:44:47.383Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).