A Backdrivable Kinematically Redundant (6+3)-Degree-of-Freedom Hybrid Parallel Robot for Intuitive Sensorless Physical Human–Robot Interaction
Bibliographic record
Abstract
A novel backdrivable 3-[R(RR-RRR)SR] kinematically redundant (6+3)-degree-of-freedom (DOF) spatial hybrid parallel robot with revolute actuators is proposed for low-impedance physical human–robot interaction. The kinematic model is developed based on the constraint conditions of the robot. It is shown that the type II (parallel) singularities can be completely avoided, thereby yielding a very large translational and orientational workspace. A workspace analysis is presented in order to demonstrate the capabilities of the robot. Mechanisms are then introduced to use the redundant DOF of the robot to operate a gripper with the robot actuators, which are mounted on or close to the base, thus reducing the inertia of the moving parts. The architecture of the robot makes it possible to use direct drive motors, thereby making the robot easily backdrivable and allowing the use of a very simple and effective controller. A prototype of the robot is then designed and built and the large workspace of the robot as well as the effortless physical human–robot interaction are demonstrated. The controller of the robot is then described, including a position control mode and a control mode for physical interaction, which does not require the use of a force/torque sensor or joint torque sensors. Because of its backdrivability and low moving inertia, the robot is particularly well-suited for physical human–robot interaction, as demonstrated in the accompanying videos.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".