[Submitted on 18 Aug 2026]
Title:Transferable Tool-Tissue Contact Detection from Stereo Depth in Robot-Assisted Surgery
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Abstract:Reliable tool--tissue contact detection can support interaction-aware control and downstream force estimation in robot-assisted surgery. Most existing methods learn a contact classifier from RGB appearance, which is hard to generalize. In this work, we use the depth image generated from a stereo pair to give more information about tool--tissue contact. For each depth frame, we localize a spatially supported minimum-distance patch around the tool boundary and reduce it to a single scalar, $-\log_{10}|d|$; this signal rises and falls in step with ground-truth contact. We formalize this observation with a fully supervised two-state hidden Markov model. We fit this model as a six-fold leave-one-session-out (LOSO) ensemble on six palpation sessions against a single silicone cup-like phantom, with the decision threshold selected from the pooled out-of-fold predictions. It is evaluated on four held-out sessions of three categories: 1. same task on same phantom; 2. same task on different phantom; 3. different task on different phantom. This model reaches held-out macro F1 $0.927$ and AUPRC $0.980$. We further compare against a reproduction of an RGB-based contact classifier from prior work. This RGB-based model achieves high performance on the first category (F1 $0.965$), but substantially lower performance on the other two, resulting in macro F1 $0.320$ across all four sessions. These results indicate that the tool--tissue distance is a strong, transferable cue for contact detection in robot-assisted surgery.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.18270 [cs.RO]
(or arXiv:2608.18270v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.18270
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mingyeung Wu [view email] [v1] Tue, 18 Aug 2026 19:41:16 UTC (1,428 KB)
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