Low-Resolution Perception for Robotic Packing

Abstract:This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where reconstruction cues drive next-view selection and grasp evidence updates a per-object stability estimate, jointly deciding what to acquire next and when to grasp. During the reconstruction, a low-resolution Next Best View (NBV) strategy explicitly avoids redundant views while preserving task-relevant geometry. We validate the approach in two steps: (i) an ablation study of the utility function under very low resolution, and (ii) a full end-to-end evaluation across policies, showing how low-resolution perception is a practical, scalable option for robotic packing.

6 pages, 5 figures. Accepted to IFAC World Congress 2026

Robotics (cs.RO)

arXiv:2608.25874 [cs.RO]

(or arXiv:2608.25874v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.25874 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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