ロボット梱包のための低解像度認識
Low-Resolution Perception for Robotic Packing
低コスト・低解像度の深度センサを用いたロボット梱包のためのスケーラブルな認識フレームワークを提案し、再構成の手がかりと把持の証拠を統合して次の視点選択と把持タイミングを決定する。
著者: Giuseppe Fabio Preziosa, Federico Vignoni, Chiara Castellano, Marco Faroni, Andrea Maria Zanchettin, Paolo Rocco
分類: cs.RO
原文アブストラクト
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.