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Low-Resolution Perception for Robotic Packing
Giuseppe Fabio Preziosa, Federico Vignoni, Chiara Castellano, Marco Faroni, Andrea Maria Zanchettin, Paolo Rocco
TL;DR
Robotic packing needs scalable perception despite variable objects and the limited detail of low-cost, low-resolution depth sensing. The paper combines density-aware LR–NBV with grasp-driven stability estimation to decide which views to acquire and when to grasp. Its studies show that low-resolution perception can support task-sufficient reconstruction and effective packing decisions while reducing redundant acquisitions.
Problem
Robotic packing requires scalable perception for variable objects and arrangements, while low-resolution sensing provides limited detail and may require multiple viewpoints.
Method
An object-centric pipeline combines density-aware LR–NBV with grasp-hypothesis fusion and stability by re-observation to arbitrate further viewing versus grasping.
Results
LR–NBV consistently requires fewer poses, often less than half of NBV, while maintaining comparable reconstruction percentages and improving selected IoU or size-estimation measures.
Takeaways & Limitations
Low-resolution sensing can reliably estimate object extents for packing and support grasp-ready decisions without requiring full geometry.
Abstract
from arXiv · showhide
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.
1. INTRODUCTION
Robotic packing requires perception and planning across variable objects and arrangements, but current practice remains difficult to repeat consistently. The paper addresses scalable low-resolution sensing by combining redundancy-aware view selection with grasp-driven decisions about when objects are ready to pick.
- Robotic packing must detect items, estimate dimensions, select and execute grasps, and place objects into suitable containers despite variable geometry and arrangements.
- Low-resolution sensing provides limited detail and often requires multiple viewpoints, but packing generally needs task-sufficient object dimensions rather than full 3D reconstruction.
- Partial reconstructions covering 25–30% can support robust manipulation, motivating selective acquisition rather than exhaustive geometric reconstruction.
- In multi-object packing, deciding which object to scan and when it is ready to pick is critical for throughput.
- The proposed pipeline integrates density-aware LR–NBV with grasp-hypothesis fusion to reduce redundant views and determine whether an object needs further views or can be picked.
2. RELATED WORK
The related work frames NBV as iterative viewpoint selection for reconstruction and reviews reconstruction-, hardware-, and grasp-oriented strategies. It motivates a low-resolution approach that prioritizes informative, under-represented regions while coordinating view acquisition with grasping.
- The Next Best View problem selects viewpoints that maximize scene information, commonly using model-free iterative reconstruction with volumetric representations and utility functions.
- The pipeline overview combines Object Manager decisions, Exploration/Exploitation target selection, LR–NBV pose planning, and scored 6-DoF grasp generation in a loop until objects are removed.
- Classical exploration-gain utilities and hardware-aware terms are poorly suited to low-resolution sensing because nearby views can receive similar scores and cause redundant acquisitions.
- Prior grasp-informed view-selection methods use view databases, contact-surface refinement, or pixel-wise grasp proposals, but may depend on prior data or accurate local geometry.
3. METHOD OVERVIEW
The pipeline alternates between acquiring views and executing grasps, using evolving geometry and grasp evidence to decide both actions. Its low-resolution view planner combines exploration with density-aware selection to reduce redundant acquisitions.
- Object Manager: The Object Manager decides whether to refine an object reconstruction or execute a grasp using geometric state and grasp hypotheses.Map and point-cloud updates provide geometric state, while grasp proposals are refreshed and fed back into the next decision.
- Grasp Generation: The Grasp Generator proposes candidate 6-DoF poses and quality scores from the latest point cloud at every map update.Scores reflect grasp stability and feasibility, including antipodal consistency, collision risk, and reachability.
- Pick-or-View Arbitration: Grasp stability increases when equivalent grasp proposals recur across observations, and the manager picks an object once its aggregated stability reaches the threshold.Otherwise, the policy views either the least-stable object for Explore or the most-stable object for Exploit.
- Object Identification: Object identification clusters occupancy-map points with DBSCAN, enclosing each object hypothesis in an oriented bounding box for planning and collision checking.The bounding box also defines a dynamic region of interest and objects are matched across iterations for temporal consistency.
- Low Resolution Next Best View: The LR–NBV utility evaluates candidate sensor poses as a weighted sum of Exploration and Density gains using ray-casting at the candidate resolution.Exploration measures visible volumetric information, while density weighting prioritizes under-sampled regions and discourages revisits under low-resolution sensing.
- Pick-or-View Arbitration: The policy iterates until no objects remain, selecting Pick for stable objects and View otherwise, with Explore and Exploit determining the view target.Low-resolution exploration gain alone can assign similar scores to nearby poses and produce redundant views; density gain addresses this limitation.
4. EXPERIMENTAL VALIDATION
The evaluation studies low-resolution NBV utility in reconstruction-only experiments and compares two stability-driven view policies in an end-to-end packing pipeline. Results assess accuracy, efficiency, coverage, grasp success, and failure behavior under realistic robotic conditions.
- Study A: Utility Function Validation: Study A compares LR-NBV with standard NBV and a 13-view ellipse-sampling heuristic using a shared marginal-gain stopping criterion.Each object is reconstructed five times with all methods.
- Experimental setup: The experiments use real robotic arms and time-of-flight sensors, with Study B evaluating downsampled effective resolutions R60 and R30 alongside depth-only GraspNet hypotheses.Study A uses an 8×8-pixel sensor, while Study B uses 100×100 inputs downsampled to R60 and R30.
- Study A: Utility Function Validation: LR-NBV significantly improves IoU for the horizontal box and gripper, and improves at least one bounding-box dimension for the vertical box and flange.Significance is assessed with Mann–Whitney U tests at p<0.05.
- Study A: Utility Function Validation: LR-NBV consistently uses fewer poses, often less than half as many as NBV, while maintaining comparable reconstruction percentages.The reported efficiency is attributed to prioritizing informative viewpoints and reducing redundancy.
- Study B: Full Pipeline Evaluation: Study B compares Exploration XPLR, which selects the lowest-stability cluster, with Exploitation XPLT, which selects the highest-stability cluster for early grasping.The comparison is performed in an end-to-end packing setup with three randomly posed objects transferred sequentially into a container.
Grasp success
The end-to-end evaluation records 126 successful grasps out of 192 attempts overall. Resolution affects objects differently: R60 helps larger parts, while R30 can be more reliable for small reflective parts.
- Overall result: 126 successes (65.6%) and 66 failures (34.4%) were observed overall.The totals cover all reported conditions.
- Resolution and object properties: R60 improves reconstruction and grasping on the larger Obj A by producing denser, more coherent geometry for pose estimation and grasp ranking.The passage attributes the difference to Obj A’s larger size.
- Resolution and object properties: R30 often outperforms R60 on the smaller reflective Obj B because lower resolution reduces false-reflection points that confuse clustering.The passage characterizes lower resolution as mild spatial smoothing.
Surface reconstruction
At the grasp decision point, the pipeline produces partial rather than exhaustive reconstructions. Mean surface reconstruction remains between 40% and 60% across policy–resolution pairs and both objects.
- Mean reconstruction at grasp time: 40–60% mean surface reconstruction is achieved at grasp time across all policy–resolution pairs and both objects.The measurement is taken when each object is judged grasp-ready.
- Mean reconstruction at grasp time: The observed coverage matches the design goal of a partial, grasp-ready model while avoiding under-reconstructed cases below 40%.The passage states that below-40% cases can produce possible but unstable grasp hypotheses.
Grasp failures
Failures are categorized by stability–readiness, spurious points, insufficient observation or segmentation, and external constraints. STAB is the largest highlighted category and varies by policy.
- Failure categories: STAB failures occur when a grasp appears stable on a locally reconstructed patch but execution fails because essential regions remain unreconstructed.This category captures grasp-readiness decisions that are inconsistent with the full object.
- Failure categories: SPUR failures arise from grasps proposed on sensor-noise points, often amplified by reflective surfaces.UNSEEN covers insufficient observation or segmentation, while EXT covers motion-planning or workspace constraints.
- Failure breakdown: 33.3% of all failures are STAB; XPLR averages 37.5% STAB versus 29.4% for XPLT.The passage links the lower XPLT rate to stricter object-specific verification of grasp readiness.
- Failure breakdown: Low-cost, low-resolution sensing produces errors strongly influenced by reflectivity and ambient light, despite the stability index remaining effective overall.The failure breakdown identifies sensing noise as a major error source.
Acquisition curves
Figure 5 shows that higher-resolution sensing generally requires fewer acquisitions before grasp, while acquisition counts decline across successive grasps as earlier views contribute evidence to remaining objects.
- Higher-resolution R60 requires fewer acquisitions than R30 before grasp across both acquisition curves.The two R30 curves lie above their corresponding R60 curves.
- Acquisition requirements decrease for Objects #2–#3 because earlier views already contribute evidence for remaining items.The curves exhibit a sublinear progression after grasping Object #1.
- XPLR follows a clearer logarithmic acquisition pattern by lifting the least-reconstructed item at each step.This produces a flatter decay in the depth curves.
- XPLT is closer to linear because it focuses on one object until grasp, potentially leaving the next object with little prior evidence.Its policy can make the next object start nearly from scratch.
5. CONCLUSION
The paper concludes that low-resolution, low-cost perception can support robotic packing through object-centric reconstruction and grasp-stability decisions without requiring full geometry.
- Low-resolution, low-cost perception is presented as a viable solution for robotic packing.
- The pipeline combines low-resolution NBV, grasp-driven reconstruction, and stability by re-observation to decide when to grasp.
- Experiments show that low-resolution sensing can reliably estimate object extents for packing without reconstructing full geometry.
- Future work targets highly reflective objects by sensing problematic regions more efficiently.