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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".