External torque virtual sensors applied to a 6-DOF robot arm
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
Robotic manipulators are arguably the most important engineering feats for future automation, enabling in-space construction, automating maintenance tasks, and advancing industrial manufacturing. Further advancements in simulation software, computational power, machine learning approaches, and emerging trends of cyber-physical systems are increasing the demands for data to improve control and safety systems onboard. A supplement to these demands is virtual sensors or soft sensors, which produce signals like physical sensors based on a predefined architecture and algorithm. Virtual sensors encompass topics such as sensor fusion algorithms and estimation theory but also enable measurement of properties that lack a physical sensor counterpart. Some advantages of virtual sensors that can benefit robotics include redundancy, reliability, adaptability, and cost reduction. All of these properties stem from the fact that there is no physical hardware onboard the robot, ultimately avoiding wear, drift, and maintenance. This paper leverages virtual sensors as an external torque sensor at the end-effector using a combination of the system’s dynamics and external observers, specifically the Unscented Kalman Filter (UKF), the first order-momentum observer (FOMO), and the General Momentum Kalman Filter (GMKF), on a simulated six-degree-of-freedom UR5 robot arm.
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