Stability of cooperative teleoperation using haptic devices with complementary degrees of freedom
Bibliographic record
Abstract
In bilateral teleoperation of a dexterous task, to take full advantage of the human's intelligence, experience and sensory inputs, a possibility is to engage multiple human arms through multiple masters (haptic devices) in controlling a single‐slave robot with high degrees‐of‐freedom (DOF); the total DOFs of the masters will be equal to the DOFs of the slave. A multi‐master/single‐slave cooperative haptic teleoperation system with w DOFs can be modelled as a two‐port network where each port (terminal) connects to a termination defined by w inputs and w outputs. The stability analysis of such a system is not trivial because of dynamic coupling across the different DOFs of the robots, the human operators and the physical or virtual environments. The unknown dynamics of the users and the environments exacerbate the problem. The authors present a novel, straightforward and convenient frequency‐domain method for stability analysis of this system. As a case study, two 1‐DOF and 2‐DOF master haptic devices are considered to teleoperate a 3‐DOF slave robot. It is qualitatively discussed how such a trilateral haptic teleoperation system may result in better task performance by splitting the various DOFs of a dexterous task between two arms of a human or two humans. Simulation and experimental results demonstrate the validity of the stability analysis framework.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".