Commercial Grasping Datasets: From Lab Benchmarks to Production Pick Rates
Production grasping systems fail not because of algorithm limitations but because training data does not reflect the object diversity, clutter density, and lighting variability of real warehouse and manufacturing floors. Public grasping benchmarks use curated object sets in controlled conditions, producing models that achieve 95%+ in the lab and 70% on the line.
Why Do Lab-Trained Grasping Models Fail in Production?
Robot grasping has been studied for decades, yet deploying reliable grasping in unstructured commercial environments remains an open problem. AnyGrasp demonstrated a generalizable 6-DoF grasp detection framework that transfers across grippers and objects, achieving strong results on benchmark datasets. However, the authors acknowledged that performance degrades significantly on objects with novel materials, transparent surfaces, and extreme aspect ratios not represented in training data. The Dex-Net project produced a series of increasingly sophisticated grasping planners trained on large synthetic datasets, with Dex-Net 4.0 achieving 95% grasp success on known objects. Yet the sim-to-real gap remains: synthetic training data cannot capture the material properties, deformability, and surface textures that determine grasp stability on real commercial products. The pattern across grasping research is consistent: model generalization is bounded by the diversity of objects and conditions in the training set.
[1][2]What Makes Commercial Grasping Data Different from Research Benchmarks?
Research grasping benchmarks like the Cornell Grasping Dataset, Jacquard, and GraspNet-1Billion use curated object sets in controlled lighting with clean backgrounds. Commercial grasping operates under fundamentally different conditions: bins filled with hundreds of mixed SKUs, reflective packaging, deformable bags, transparent bottles, and varying illumination across shifts. UniGraspTransformer showed that training on diverse object geometries with varied gripper configurations improved zero-shot grasping success, but the evaluation was still conducted in controlled environments with rigid objects on clean surfaces. The gap between benchmark conditions and production floors — where objects are stacked, occluded, damaged, or wet — accounts for the 20-30 percentage point drop in pick success rates that commercial deployments consistently report.
[4][3]How Does Object Diversity Limit Current Grasping Datasets?
GraspNet-1Billion provided 1 billion grasp poses across 88 objects, establishing a large-scale benchmark for 6-DoF grasp detection. But 88 objects cannot represent the tens of thousands of SKUs in a typical e-commerce fulfillment center. AnyGrasp addressed this by training on both synthetic and real data with diverse object geometries, yet the real-world component was limited to objects available in research settings. Commercial grasping requires training data spanning product categories that research labs rarely handle: food packaging with variable fill levels, pharmaceutical blister packs, textiles in polybags, cosmetics with irregular shapes, and electronics in anti-static wrap. Each category introduces distinct grasp failure modes that must be represented in training data for production-viable pick rates.
[3][1]How Do Open Grasping Datasets Compare to Commercial-Grade Data?
The table below compares major open grasping datasets against Claru custom collection. The critical differentiators for commercial deployment are object diversity, environment realism, and coverage of failure-mode conditions.
GraspNet-1Billion
Dex-Net (Synthetic)
Cornell Grasping Dataset
OCID-Grasp
Claru Custom
Annotators
Countries
Annotations Delivered
QA Turnaround
Frequently Asked Questions
A commercial grasping dataset is training data collected on real products in real operational environments such as warehouses, fulfillment centers, and production lines. Unlike research benchmarks that use curated rigid objects in controlled lighting, commercial datasets capture the full range of conditions that production grasping systems encounter: deformable packaging, transparent bottles, reflective surfaces, dense clutter, and variable lighting across work shifts.
Object coverage is configured per engagement based on the client's specific SKU mix and deployment environment. Claru's global contributor network can capture grasping data across any product category. Previous data collection programs have covered workplace tools, kitchen implements, commercial products, and consumer goods across 10+ workplace categories in multiple countries.
Yes. Claru's annotation taxonomy includes grasp outcome labels covering success, slip, crush, miss, and collision failure modes alongside environmental condition annotations. Failure-mode data is critical for production pick rate improvement because reducing failure cases often provides larger gains than improving best-case performance.
Claru configures output formats to match your model's expected input representation. Supported formats include 6-DoF grasp poses, planar grasp rectangles, grasp quality scores, contact point annotations, and force-torque labels when sensor data is available. Annotation schemas are co-developed with the research team through iterative revision cycles.
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References
- [1]Fang et al.. “AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains.” IEEE T-RO 2023, 2023. Generalizable 6-DoF grasp detection framework transferring across grippers and objects; noted performance degradation on novel materials and transparent surfaces not in training data. Link
- [2]Mahler et al.. “Dex-Net 4.0: Learning Ambidextrous Robot Grasping Policies.” Science Robotics 2019, 2019. Large-scale synthetic grasping dataset and planner achieving 95% success on known objects; acknowledged persistent sim-to-real gap on material properties. Link
- [3]Fang et al.. “GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping.” CVPR 2020, 2020. 1 billion grasp poses across 88 objects establishing a large-scale 6-DoF grasp detection benchmark; limited object diversity relative to commercial applications. Link
- [4]Xu et al.. “UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Grasping.” arXiv 2023, 2023. Demonstrated that training across diverse object-gripper combinations improved zero-shot grasping success through policy distillation from privileged to vision-based policies. Link