Bibliographic record
Abstract
In the Motion Research Group at the University of Waterloo, we investigate the dynamics, model-based control, and design optimization of multibody biomechatronic systems. Deriving the equations for these complex systems is both tedious and error-prone, so we have automated the dynamic modelling process by combining linear graph theory from mathematics with fundamental principles from physics and biology. Our symbolic computer implementation of this approach, now part of MapleSim, will generate real-time simulation code and dynamic controllers for systems ranging from biomechatronic devices to autonomous vehicles. In this talk, I will discuss some advantages of a symbolic graph-theoretic approach to systems modelling and control, and highlight these advantages through a large number of real-world applications that include a plug-in Prius for Toyota, a rover for the Canadian Space Agency, a hockey slapshot robot, and a stroke rehabilitation robot that uses an integrated model of the patient's musculoskeletal system and robot to determine optimal control actions. Both modelling and experimental results will be presented to show the real-world performance of the model-based designs and controllers. Future applications to autonomous vehicles and biomechatronic assistive devices will be discussed.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".