发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合重建线索与稳定性估计的低分辨率感知框架，通过最佳下一个视角（NBV）策略实现机器人装箱，经 ablation 研究与端到端评估验证，该方法实用可扩展。
AI 中文摘要
本研究针对采用低成本低分辨率深度传感的机器人装箱，解决可扩展感知问题。我们提出一种框架，其中重建线索驱动下一个视角选择，抓取证据更新每个物体的稳定性估计，共同决定下一步采集内容及抓取时机。重建过程中，低分辨率最佳下一个视角（NBV）策略明确避免冗余视角，同时保留任务相关几何信息。我们分两步验证该方法：（i）在极低分辨率下对效用函数进行 ablation 研究；（ii）跨策略进行完整端到端评估，证明低分辨率感知是机器人装箱实用且可扩展的选项。
英文摘要
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.
Comments6 pages, 5 figures. Accepted to IFAC World Congress 2026
