Contact Force Estimation of Continuum Robots without Embedded Sensors: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Continuum robots enable safe and adaptive interaction with complex, unstructured, or constrained environments through continuous deformation, making them particularly suitable for medical and industrial applications. Accurate contact force sensing is essential to ensure safe and effective physical interaction in such scenarios. Although various embedded force sensors have been developed, sensor‐free approaches offer advantages in miniaturization, cost‐effectiveness, and biocompatibility. This review provides a comprehensive overview of sensor‐free contact force estimation methods for continuum robots, with an emphasis on algorithmic principles rather than specific continuum robot designs or applications. First, contact forces reported in the literature are systematically classified according to their distribution, components, and dynamics. Next, existing force estimation methods are divided into three categories: actuation‐based, deflection‐based, and environment‐based. For each category, the underlying algorithmic principles are discussed, representative challenges are highlighted, and their typical application scenarios are outlined. Finally, emerging trends and potential directions for future research are outlined.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it