The paper gives an SVM sample-complexity bound for learned collision detection in terms of configuration-space clearance, but the proposed guarantee algorithm rests on an unjustified monotonicity assumption in its termination proof.
Sampling-based robot motion planning,
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From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection
The paper gives an SVM sample-complexity bound for learned collision detection in terms of configuration-space clearance, but the proposed guarantee algorithm rests on an unjustified monotonicity assumption in its termination proof.