Explicit compliance and safety on torque controlled robots for physical interaction
Bibliographic record
Abstract
Robots operating in close physical contact must reconcile high-precision motion with context-dependent compliance and uncompromising safety. We introduce a quadratic programming (QP) torque-control framework that incorporates external torque estimates into the inverse dynamics to compute accurate torque commands, enabling proper acceleration tracking under external disturbances. The proposed QP framework embeds an explicit compliance parameter, allowing selective and continuously tunable compliance and effective inertia shaping at both the task and joint levels.We further revisit the parametrization of acceleration-based formulations for position and velocity constraints, removing their dependency on the control frequency. Specifically, we propose a second-order velocity damper based on two intuitive parameters, and provide an experimental analysis of their effects. This study reveals that classical formulations relying on the control loop frequency are not suitable and provides guidance on selecting appropriate parameters for a given system.The framework is validated on a Kinova Gen3 across diverse interaction scenarios, ranging from stiff, null-space compliance to full-body compliance, demonstrating advanced behaviors such as task-space inertia shaping, while ensuring safety under unpredictable external forces.These results suggest that coupling explicit compliance with external force-aware QP optimization offers a practical and versatile approach to achieving precise and adaptive physical human–robot interaction.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".