Joint Compliance Control of Biped Robot Considering Position Tracking and Task Priority
Bibliographic record
Abstract
In this work, we propose a joint force control framework for the stable motion of a humanoid biped robot. In motion planning, nonlinear centroid dynamics is used to generate gait on uneven terrain, which overcomes the limitation of a linear inverted pendulum (IP) model on centroid height. The motion control layer combines multipriority inverse kinematics (MPIK) and multipriority dynamic control (MPDC). The MPIK uses a multipriority inverse kinematics numerical iteration algorithm to calculate joint position command. The MPDC uses a multipriority iterative optimization method based on the task-space dynamics model on the forward path, which does not need preallocation or preoptimization of contact force, does not explicitly control the movement of center of mass (CoM), and tries its best to complete high-priority tasks. Finally, a stable joint compliance force control framework is built, and the introduction of kinematic error information ensures the accurate position tracking of the force control system. The results show that the control strategy completes the task of climbing stairs well and shows a certain antidisturbance ability in standing still and variable speed walking. The maximum disturbance in the sagittal plane can reach 50 N·s (achieved solely by adjusting the position of the pressure center and without using the step stability strategy).
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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".