Adaptive teleoperation using neural network-based predictive control
Bibliographic record
Abstract
Teleoperation systems strive to accurately render often unstructured environments to operators. However, due to the existing delays in the communication channel, transparent performance and stability are compromised. This paper presents a new class of teleoperation predictive controllers, in which the dynamics of the environment is mapped and simulated at the master side using two neural networks. The supervised network at the slave side is trained online to generate environment contact force using slave contact position and force. The master network whose gains are adaptively updated online with the transmitted slave network gains, replicate the environment force using master position. The estimated environment force is utilized in a "pseudo" two-channel force-position bilateral teleoperation control architecture. The proposed controller does not require an environment model to reflect environment dynamics for transparency. Thus, it can be used for operations on unstructured environments displaying varying nonlinear dynamic behavior. The improved performance of the new teleoperation architecture in comparison with that of a conventional two-channel force-position architecture that uses measured environment force for feedback is verified on a teleoperation test-bed consisting of two planar Twin-Pantograph haptic devices
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".