Uncertainty-Aware Stereo Grasp Point Selection for Deformable Linear Objects

Cristina Saccani, Alessio Caporali, Gianluca Palli

Keywords: Deformable Linear Objects, Robotic Grasping, Stereo Vision, Uncertainty Estimation, Deep Learning

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.

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Submission Number: 20