Benchmarking and Workload Analysis of Robot Dynamics Algorithms
Bibliographic record
Abstract
Rigid body dynamics calculations are needed for many tasks in robotics, including online control. While there currently exist several competing software implementations that are sufficient for use in traditional control approaches, emerging sophisticated motion control techniques such as nonlinear model predictive control demand orders of magnitude more frequent dynamics calculations. Current software solutions are not fast enough to meet that demand for complex robots. The goal of this work is to examine the performance of current dynamics software libraries in detail. In this paper, we (i) survey current state-of-the-art software implementations of the key rigid body dynamics algorithms (RBDL, Pinocchio, Rigid-BodyDynamics.jl, and RobCoGen), (ii) establish a methodology for benchmarking these algorithms, and (iii) characterize their performance through real measurements taken on a modern hardware platform. With this analysis, we aim to provide direction for future improvements that will need to be made to enable emerging techniques for real-time robot motion control. To this end, we are also releasing our suite of benchmarks to enable others to help contribute to this important task.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".