Unimodal asymmetric interface for teleoperation of mobile manipulators: A user study
Bibliographic record
Abstract
There is demand to develop methods and interfaces for teleoperation of complex systems in mission-critical applications. In this paper, we study three different methods to command a one-arm mobile manipulator from a 6-DOF input device capable of haptic feedback. The linkage between the master and the slave devices is asymmetric, that is, the input haptic interface (master) is much smaller and has different kinematics and dynamics from the robot arm and the mobile base (slave). Three different master-slave motion coordination schemes are compared here (1) workspace clutching, (2) differential end-zone, and (3) position/rate switching. We study repetitive user performance for seven subjects in a static Tower of Hanoi manipulation task and present single case studies for two mobile manipulation tasks: door opening and large-displacement Towers of Hanoi. Our experimental platform consists of a 4-DOF WAM (Whole Arm Manipulator) on a Segway RMP (Robotic Mobility Platform) controlled by a Phantom Omni haptic device. Cameras are used to relay scene images to the remote operator. The human stays in the loop throughout the entire task. The results obtained from user studies provide insight on how to interface and command a mobile manipulator.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".