Torque Estimation for Robotic Joint With Harmonic Reducer Based on Deformation Calibration
Bibliographic record
Abstract
Joint torque sensing is an important technique for high-performance control of modern robotic systems, especially in an environment that requires man-machine collaboration. However, in many cases, traditional torque sensors are not suitable for robots because of the inevitable increase of joint flexibility and joint size. To address this problem, two novel methods are proposed to estimate torque of robotic joint with harmonic reducer via calibration of its existing flexibility without the need for any additional elastic elements. The first approach utilizes a new harmonic drive compliance model, which is more convenient for calibration and less dependent on the manufacturer's parameters to estimate the output torque. The second method relies on a system based on a back-propagation (BP) neural network to fit the non-linear relationship among the output torque, motor current, and other information that can be obtained from double encoders mounted on motor-side and load-side. The two proposed methods were experimentally investigated and the results show that the estimated torque values were in good agreement with the measurements obtained using a commercial torque sensor. Finally, different suitable application scenarios are presented according to the specific performance of each technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".