Model-Based Coupling for Co-Simulation of Robotic Contact Tasks
Bibliographic record
Abstract
Co-simulation of complex robotic systems allows the different components to be modeled and simulated independently using methods and tools tailored to their nature and time-scale, which makes the implementation process more modular and flexible. Some applications require the use of non-iterative coupling schemes for optimal performance, such as real-time interactive environments and human and hardware-in-the-loop setups. Stability of non-iterative schemes is challenging due to the restricted and delayed information that is exchanged between subsystems, and robust prediction of interface variables is key. Here, we propose a framework for exchanging model information between mechanical systems with contact, where reduced-order models approximate the interface dynamics of the subsystems. Effective mass and force terms are formulated using a reduced representation of the model, which can then be exchanged between subsystems and integrated in their simulation. The analysis of several simulations of challenging robotic contact tasks, such as grasping and insertion with jamming, shows that model-based coupling allows for stable co-simulation with larger interface stiffness values, resulting in stronger coupling and higher simulation accuracy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".