Task Performance Evaluation of Asymmetric Semiautonomous Teleoperation of Mobile Twin-Arm Robotic Manipulators
Bibliographic record
Abstract
A series of human factors experiments involving maneuvering and grasping tasks are carried out to evaluate the effectiveness of a novel asymmetric semiautonomous teleoperation (AST) control design framework for teleoperation of mobile twin-arm robotic manipulators. Simplified configurations are examined first to explore control strategies for different aspects of such teleoperation tasks. These include teleoperation of a nonholonomic mobile base, telemanipulation of a dual-arm robot, and dual-arm/dual-operator teleoperation task scenarios. In two sets of experiments with a planar nonholonomic mobile base, teleoperation via a 3DOF planar haptic interface with position mapping and force reflection of the nonholonomic constraint decreases task-completion-time (TCT) and reduces unwanted collisions. In dual-arm and dual-operator teleoperation maneuverability experiments, the assignment of decoupled and nonconflicting control frames reduces TCT and unwanted contacts. The use of so-called "soft" constraints via passive semiautonomous control reduces TCT and unwanted block drops in telegrasping experiments with a twin-arm manipulator. A final comprehensive experiment encompassing elements of the simplified configurations demonstrates the effectiveness of AST control framework in dual-operator teleoperation of a twin-arm mobile manipulator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".