Control of constrained robots subject to unilateral contacts and friction cone constraints
Bibliographic record
Abstract
The projection-based control of a rather general class of robots subject to linear and quadratic inequality constraints pertinent to unilateral contacts and friction cones is presented. The controller can also take into account other physical constraints levied by actuator saturation limits and/or existence of some unactuated joints, and can minimize actuation effort for redundant systems. Moreover, since the controller is not based on derivation of minimal-order dynamics model, it can easily handle contact switching. Therefore, a single controller can be used for different constraint conditions, which is very appealing for applications such as legged robots or grasping robots. The orthogonal decomposition of the generalized force vector yields the primary and secondary control inputs, which are used for motion control and interaction control, respectively. It is shown that the problem of minimizing actuation effort subject to the constraints due to the friction cones, unilateral contacts, and actuation limitations, can be transcripted into an optimization programming in terms of the secondary control variable. The latter optimization problem has a quadratic cost function accompanied by a set of quadratic and linear inequality constraints plus linear equality constraints that can be solved by barrier methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".