Trilateral teleoperation control of kinematically redundant robotic manipulators
Bibliographic record
Abstract
Teleoperation control of kinematically redundant robots requires a strategy for resolving their redundancy. A trilateral two-master/one-slave control approach is proposed for delay-free applications in which the first master controls a primary task control frame, e.g. the slave end-effector frame; meanwhile, another master device can manipulate a secondary task frame attached to the slave robot, e.g. to avoid collision with obstacles in the task environment. Any remaining degrees of motion are resolved autonomously. Teleoperation control is achieved in three steps employing joint-space Lyapunov-based adaptive motion/force controllers, a velocity-level redundancy resolution method, and task-space coordinating reference commands. Priority can be given to either the primary or secondary control frame so that the high-priority task can be transparently carried out without interference from the other task. Whenever applicable, the lower-priority task control frame would be restricted to the natural constraints imposed by prioritization or otherwise, decoupling between the tasks is achieved with the use of an arbitrarily weighted pseudo-inverse. Experiments with a planar teleoperation system consisting of two master devices controlling a closed-chain four degree-of-motion redundant slave robot show the feasibility of the approach.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".