Exploiting the Kinematic Redundancy of a Backdrivable Parallel Manipulator for Sensing During Physical Human-Robot Interaction
Bibliographic record
Abstract
Robots need to adapt their behaviour while physically interacting with an operator to guarantee safety and provide intuitiveness. Inferring the intentions of the operator is a challenging problem that can be addressed by introducing sensors, in addition to motor encoders. Also, kinematic redundancy can be used to avoid issues such as singularities or mechanical interference, and the redundant coordinates can be controlled freely. In this work, we propose to use the redundant degrees of freedom to infer the intentions of an operator interacting with a backdrivable kinematically redundant parallel robot, without introducing any additional sensors. The proposed approach is based on the fact that, in mechanically backdrivable robots, the operator can control the redundant degrees of freedom, and this can be sensed using solely motor encoders through the solution of the forward kinematics. This approach is implemented to switch between a position controller and a controller that allows the operator to guide the robot freely thanks to gravity compensation. Experiments are carried out to compare this approach with an existing one and show that it improves intuitiveness during interaction by reducing false mode change detections.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".