End-Point Contact Force Control with Quantitative Feedback Theory for Mobile Robots
Why this work is in the frame
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Bibliographic record
Abstract
Robot force control is an important issue for intelligent mobile robotics. The end-point stiffness of a robot is a key and open problem in the research community. The control strategies are mostly dependent on both the specifications of the task and the environment of the robot. Due to the limited stiffness of the end-effector, we may adopt inherent torque to feedback the oscillations of the controlled force. This paper proposes an effective control strategy which contains a controller using quantitative feedback theory. The nested loop controllers take into account the physical limitation of the system's inner variables and harmful interference. The biggest advantage of the method is its simplicity in both the design process and the implementation of the control algorithm in engineering practice. Taking the one-link manipulator as an example, numerical experiments are carried out to verify the proposed control method. The results show the satisfactory performance.
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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 | 0.001 | 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 | 0.001 |
| 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