Achieving Velocity Tracking Despite Model Uncertainty for a Quadruped Robot with a PD-ILC Controller
Bibliographic record
Abstract
In this study, we introduce a control strategy that combines Proportional-Derivative (PD) control with Iterative Learning Control (ILC) to enhance legged robot velocity control with only the inverse kinematics and no additional system identification. This approach leverages the realtime feedback capabilities of PD control for gait tracking while incorporating ILC's learning abilities to eliminate inaccuracies from unmodeled dynamics iteratively and to reach desired velocities without residual errors. By uniting these techniques, the proposed method empowers legged robots to adapt and optimize their control behavior, achieving and maintaining desired walking velocities. Experimental results on the physical legged robot Go1 demonstrate the effectiveness of the proposed approach, highlighting its adaptability and reliability in real-world scenarios. This research represents a first step towards overcoming high computational effort and extensive data collection for quadruped robot velocity tracking through onboard learning.
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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".