Task Level Modelling of Mechanical Systems for Intelligent Robotics: Plenary Paper
Bibliographic record
Abstract
Robotics generally includes complex mechatronic systems where the mechanical elements form a subsystem. The type of robotic systems significantly expanded in recent years. However, they usually share one common element; the main function of a robotic system includes motion and/or force, i.e., the mechanical function is primarily the distinguishing feature of a robotic device. Significant progress took place in the software and sensing components of robotic systems in the past few years leading to more intelligent abilities. On the other hand, the mechanical subsystem behaviours, and how they can affect the performance of the functioning of a robot, do not seem to be completely understood and investigated in detail. This fact manifests itself in multiple ways, for example, in many robotic systems the mechanical elements are the first to break down. Also, many robotic systems with advanced software components have very poorly performing mechanical parts. Comparing it with the automotive industry, robotic systems are far less reliable mechanically. Based on the same comparison one can draw the conclusion that not enough effort has been invested to understand the mechanics of the different robotic devices and functions. In this work we present a task-specific modelling and analysis approach to provide insight into the mechanics of various types of robotic systems. This starts from the broader interpretation of some main concepts of mechanical modelling, and leads to the possibility to establish taskoriented models and performance measures for both static and dynamic behaviours. We illustrate the material with several examples of different robotic tasks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".