Development of a Flexible Assembly System for the World Robot Summit 2020 Assembly Challenge
Bibliographic record
Abstract
The assembly challenge of the World Robot Challenge (WRC) 2020, which was a part of the World Robot Summit (WRS) 2020, aimed to complete rapidly changing tasks in high mix/low volume production through building agile and lean production systems that can respond to one-off products. The authors of this paper participated in the challenge with the team PneuBot from the Industrial Robotics Facility of the Italian Institute of Technology by developing a flexible assembly system. The purpose of this work was to develop an assembly system able to handle variations of parts and tasks with a minimal changeover in hardware and software. In particular, assembly tasks were carried out, such as the assembly of a DC motor, pulleys, and a flexible belt on a plate, starting from pieces of unknown positions and orientations on a tray. The proposed work cell is light-weighted and can be fast deployed and replicated. It is composed of two Universal Robots; an RGB-D camera mounted on the wrist of the robot, able to detect both the position and orientation of the different objects to manage; a custom gripping system composed of 3D printed fingers for manipulation purposes and miniature force sensors for the grasping detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".