# Tool-Tissue Interaction

Surgical robots need to anticipate how instrument motion will deform tissue when visibility is interrupted, and prior differentiable physics simulation predicted absolute tissue positions, so reconstruction error, initialization, and simulation offsets left the whole surface persistently displaced. The forecast now anchors each tissue point to its last valid observation and predicts only the deformation induced by the known future tool trajectory, using mesh-free position-based dynamics with scene-specific rigidity, attachment, and grasp coupling. Across 12 surgical manipulation clips, the referenced forecast has a median within-clip error ratio of 0.912 against the persistence baseline, 95% interval 0.787–1.070, improving 8 of 12 clips, with the interval including parity.

Surgical robots need to anticipate how instrument motion will deform tissue when visibility is interrupted, and prior differentiable physics simulation predicted absolute tissue positions, so reconstruction error, initialization, and simulation offsets left the whole surface persistently displaced. The forecast now anchors each tissue point to its last valid observation and predicts only the deformation induced by the known future tool trajectory, using mesh-free position-based dynamics with scene-specific rigidity, attachment, and grasp coupling. Across 12 surgical manipulation clips, the referenced forecast has a median within-clip error ratio of 0.912 against the persistence baseline, 95% interval 0.787–1.070, improving 8 of 12 clips, with the interval including parity.

The work proposes Surgical Action World (SAW), which reformulates video-to-video diffusion as trajectory-conditioned surgical action synthesis conditioned only on four lightweight signals—a language prompt, a reference first frame, a tissue affordance mask, and 2D tool-tip trajectories—fine-tunes LTX-Video on a custom-curated dataset of 12,044 laparoscopic clips with a depth consistency loss, reaches CD-FVD 199.19 (vs. 546.82 for SurgSora) and FVD 224.28 on held-out test data, and demonstrates that augmenting rare actions with generated videos improves action recognition on real test data (clipping F1 20.93% to 43.14%; cutting 0.00% to 8.33%) plus initial feasibility of rendering tool-tissue interaction videos from simulator-derived trajectories.

The work proposes Surgical Action World (SAW), which reformulates video-to-video diffusion as trajectory-conditioned surgical action synthesis conditioned only on four lightweight signals—a language prompt, a reference first frame, a tissue affordance mask, and 2D tool-tip trajectories—fine-tunes LTX-Video on a custom-curated dataset of 12,044 laparoscopic clips with a depth consistency loss, reaches CD-FVD 199.19 (vs. 546.82 for SurgSora) and FVD 224.28 on held-out test data, and demonstrates that augmenting rare actions with generated videos improves action recognition on real test data (clipping F1 20.93% to 43.14%; cutting 0.00% to 8.33%) plus initial feasibility of rendering tool-tissue interaction videos from simulator-derived trajectories.