Experimental evaluation of adaptive CMAC haptic control for teleoperation of compliant-joint manipulators
Bibliographic record
Abstract
Using compliant joints and haptic feedback are two methods that could improve next-generation teleoperated systems, but both methods present challenges to traditional control systems. This paper describes the first experimental investigation of a new approach to real-time haptic control for teleoperated robots with compliant joints. One original aspect of the approach is using an auxiliary error, in which a velocity penalty is added to the force error. Also, the method utilizes a Cerebellar Model Articulation Controller (CMAC), a type of neural network known for its rapid adaptation. The adaptive neural network compensates for unknown nonlinear system dynamics, interaction with unstructured environments, and non-passive operator behaviour in real-time. The auxiliary error damps vibrations and allows for control of the robot in free space without the need for control switching. In real-time experiments with both computer-generated trajectories and full bilateral teleoperation, the proposed controller tracks torque as well as a custom PID controller and outperforms the PID during free-space velocity tracking. In addition, the proposed approach causes significantly less control signal chatter than the PID during full bilateral teleoperation. A Lyapunov analysis guarantees that the proposed controller has uniformly ultimately bounded signals.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".