Uncertainty-Aware Stereo Grasp Point Selection for Deformable Linear Objects

Abstract

Reliable grasp point selection on deformable linear objects, such as cables, requires not only accurate depth estimation but also awareness of prediction reliability. We present a five-stage stereo network for joint disparity, semantic, and uncertainty estimation, and use the predicted uncertainty to filter grasp candidates before geometric ranking. Disparity uncertainty is modeled via a Laplace negative log-likelihood, semantic uncertainty via the entropy of semantic predictions, with an alignment term enforcing consistency between them. Experiments on a synthetic stereo dataset show that uncertainty-aware selection reduces the mean grasp-point depth error from 4.19 mm to 1.55 mm, increases the success rate within a 3 mm tolerance from 74.2% to 88.6%, and lowers the 90th percentile of the failure exceedance above 3 mm from 29.47 mm to 6.77 mm. These results show that uncertainty is an effective cue for safer grasp selection on deformable linear objects.