Simplified Tele-Operation of Mobile-Manipulator Systems Using Knowledge of Their Singular Configurations
Bibliographic record
Abstract
An algorithm for the simplified tele-operation of a mobile-manipulator system is presented. It allows for unified, intuitive, and coordinated control. Unlike other approaches, the mobile-manipulator system is modelled and controlled as two separate entities rather than as a whole. The algorithm consists of three states. In the first state, a joystick is used to freely control the manipulator’s position and orientation. The second state occurs when the manipulator approaches a singular configuration. This causes the mobile base to proceed in such a way as to keep the end-effector moving in its last direction. This is done through the use of a simple optimization routine. The third state is triggered by the user: once the end-effector is in the desired position, the mobile base and manipulator both move with respect to one another keeping the end-effector stationary and placing the manipulator into an ideal configuration. The proposed algorithm avoids the problems of algorithmic singularities and simplifies the control approach. A preliminary version of the algorithm has been implemented on the Jasper mobile-manipulator system with success.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".