Auditing Grasp Feasibility Predictors with Planner-Verified Counterfactuals
Overview
This research paper introduces a method to audit grasp-feasibility predictors by inserting solid obstacles into the approach corridor while holding the grasp pose fixed, revealing that many high-performing models fail to correctly use collision geometry. The authors demonstrate that across eight different predictor types and four planners, these models' accuracy drops to chance levels when subjected to counterfactual tests involving real-object obstacles. A clearance-aware read of corridor points achieves high AUROC but remains bound to trained obstacle placements, indicating that a placement-invariant representation is still an open challenge. The paper releases counterfactual labels, metrics, a datasheet, and a relabeling pipeline for further research.
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Artifact availability · code: available
“presentation is still open. We release the counterfactual labels, metrics, datasheet, and relabeling pipeline at https://github.com/codewithsajid/graspfc-cf.”
Exact source · revision 1Source owner not reported
Dataset · modalities: point clouds, voxels, depth, and object tokens
“d its points to the model input, and let a planner re-derive the label, which flips from feasible to infeasible. Across eight predictors spanning point clouds, voxels, depth, and object tokens, CF-pair AUROC falls from high natural AUROC (0.920-0.996) to chance (0.470-0.504), and the flip holds across four planners, two arms, and a real-object obstacle. Predictors fail at different resolutions, from a bounding box to”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAuditing Grasp Feasibility Predictors with Planner-Verified Counterfactuals
This research paper introduces a method to audit grasp-feasibility predictors by inserting solid obstacles into the approach corridor while holding the grasp pose fixed, revealing that many high-performing models fail to correctly use collision geometry. The authors demonstrate that across eight different predictor types and four planners, these models' accuracy drops to chance levels when subjected to counterfactual tests involving real-object obstacles. A clearance-aware read of corridor points achieves high AUROC but remains bound to trained obstacle placements, indicating that a placement-invariant representation is still an open challenge. The paper releases counterfactual labels, metrics, a datasheet, and a relabeling pipeline for further research.
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