Explicit safety and compliance on torque controlled robots for physical interaction
Bibliographic record
Abstract
This paper presents a framework aimed at improving safety and compliance in dynamic physical interactions for precise torque-controlled rigid-body robots. The framework uses a quadratic program (QP) in the motion generation formulation that explicitly accounts for external forces. Our solution ensures that adherence to feasibility and safety constraints is robust to disturbances, while preserving task compliance. Our tests on a torque-controlled Kinova Gen3 manipulator arm where we simulate external forces by attaching an uncompensated weight (1.25kg) to it's end-effector demonstrated a reduction of 100% in the violation of velocity limit. We also show that it's straightforward to choose between stiffness and compliance for each of the concurrent tasks. Furthermore, by integrating force/torque sensor measurements with a residualbased estimation, we enhanced the accuracy of interaction force estimation on average from 2.0N RMS error using only the residual estimation to 0.7N RMS error with our method. These results highlight the effectiveness of our approach in maintaining reactive safety and compliance in the presence of external forces.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".