A Kinematic Control Framework for Single-Slave Asymmetric Teleoperation Systems
Bibliographic record
Abstract
This paper is concerned with asymmetrical teleoperation, where the master/slave subsystems have different degrees of mobility. In particular, dual-master trilateral control of a possibly kinematically redundant slave robot (KRSR) and single-master control of a kinematically deficient slave robot (KDSR) are considered. In the case of a KDSR, the motion of the master robot is restricted to the natural motion constraint of the slave robot. Trilateral teleoperation is achieved via two master devices, each controlling a dedicated frame that is assigned on the slave robot. A novel control framework is presented that accomplishes two objectives: 1) motion and force tracking of the master and slave robots within their nonconstrained task spaces and 2) constrained master robot(s) motion that reflects the slave natural constraint (KDSR) or the kinematic constraint on each of the slave taskspace control frames (trilateral teleoperation). The proposed adaptive controller utilizes projection and generalized pseudoinverse matrices to achieve the stated teleoperation objectives. Stability and transparency of the asymmetric teleoperation system are demonstrated analytically and experimentally.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".