Learning Based Estimation of Tool-Tissue Interaction Forces for Stationary and Moving Environments
Bibliographic record
Abstract
Accurately estimating tool-tissue interaction forces during robotics-assisted minimally invasive surgery is an important aspect of enabling haptics-based teleoperation. By collecting data regarding the state of a robot in a variety of configurations, neural networks can be trained to predict this interaction force. This paper extends existing work in this domain based on collecting one of the largest known ground truth force datasets for stationary as well as moving phantoms that replicate tissue motions found in clinical procedures. Existing methods, and a new transformer-based architecture, are evaluated to demonstrate the domain gap between stationary and moving phantom tissue data and the impact that data scaling has on each architecture's ability to generalize the force estimation task. It was found that temporal networks were more sensitive to the moving domain than single-sample Feed Forward Networks (FFNs) that were trained on stationary tissue data. However, the transformer approach results in the lowest Root Mean Square Error (RMSE) when evaluating networks trained on examples of both stationary and moving phantom tissue samples. The results demonstrate the domain gap between stationary and moving surgical environments and the effectiveness of scaling datasets for increased accuracy of interaction force prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".