Physical Human–Robot Interaction Using a Macro–Mini Robotic System
Bibliographic record
Abstract
In this article, we investigate the characteristics of a collaborative hybrid parallel robot. A comparison between this robot and other collaborative robots regarding the Cartesian inertia is presented to highlight the very low inertia of the hybrid parallel robot. A task space stiffness-damping control is also proposed. In order to improve the performance of macro--mini systems in the interaction context, an active macro-active mini system consisting of the hybrid parallel robot mounted on a three degrees of freedom (3-DOF) gantry translational robot is introduced. A control strategy for physical human–robot interaction is applied to the macro--mini arrangement. The stability of this method is analyzed and it is shown that the macro–mini combination is more stable than the mini alone. An experimental validation is then carried out. The results obtained with the hybrid parallel robot show that the desired interaction force can be tracked at high speed and that prescribed impedance parameters can be precisely rendered to the human operator without using any additional sensor. Also, it is pointed out that the bandwidth of interaction of the hybrid parallel robot, as well as of the macro--mini system is much higher than that of other commercial collaborative robots. In addition, other tests are realized on the macro–mini system in order to verify the performance and to demonstrate potential applications. The macro–mini robot introduced in this work yields a very intuitive human–robot interaction, which makes it ideal for many applications in which direct physical teaching or assistance is needed.
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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 | 0.000 | 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 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".