Search papers, labs, and topics across Lattice.
This paper addresses the challenge of scalable perception in robotic packing by utilizing low-cost, low-resolution depth sensing. The authors introduce a framework that integrates reconstruction cues for next-view selection with grasp evidence to update per-object stability estimates, optimizing the decision-making process for when to acquire data and grasp objects. Validation through an ablation study and an end-to-end evaluation demonstrates that low-resolution perception can effectively support robotic packing tasks without sacrificing performance.
Low-resolution depth sensing can enable efficient robotic packing by optimizing perception and grasping decisions, proving that less can be more in complex tasks.
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.
