Dual-master teleoperation control of kinematically redundant robotic slave manipulators
Bibliographic record
Abstract
Kinematically redundant robotic manipulators (KRRM) can provide a great degree of flexibility for working in complex unstructured environments. Teleoperation control of KRRM requires a strategy to resolve the redundancy of the slave robot while achieving transparency in the task space. In this paper, a two-master control approach is proposed in which the first master transparently controls the redundant slave end-effector in the task space, denoted as the primary task. Meanwhile, a second master exploits the slave redundancy to perform a secondary task such as obstacle avoidance or internal position control. Kinematic redundancy is considered for the slave robot and the traditional autonomous null-space control approach is also accommodated. Teleoperation control is achieved in two steps. First, velocity-level redundancy resolution is attained through new joint-space Lyapunov-based adaptive motion/force controllers. Coordinating reference commands for the joint-space controllers are designed to give priority to the primary task and decoupling between the tasks is achieved without the use of a dynamically consistent pseudo-inverse. Experimental results with two identical planar two-degree-of-freedom master devices controlling a simulated four-degree-of-freedom redundant slave robot show the effectiveness 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 | 0.000 |
| 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) | 0.001 | 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".