Impact Force Reduction Strategies To Achieve Safer Human-Robot Collisions
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Bibliographic record
Abstract
The increasing use of robots operating close to people has made human-robot collisions more likely. In this paper, strategies intended to reduce the impact force to a safe level, without sacrificing the robot's performance, are investigated. The strategies can be applied to a robot arm without modifying its internal hardware. They include the existing strategies: lowering the actuator controller's stiffness; actuator switched off upon impact detection; withdrawing the arm upon impact detection; and adding a compliant cover. We also propose the novel strategy of limiting the controller's feedback term. The collision scenario studied is a robot arm colliding with a person's constrained head. An improved lumped parameter model of the constrained impact is proposed. Simulation results are included for a UR5 collaborative robot. Sixteen combinations of the impact force reduction strategies are compared. The results show that using a high stiffness controller with a feedback limit and compliant cover reduces the impact force to a safe level, and achieves precise trajectory tracking.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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