Development of a Flexible Assembly System for the World Robot Summit 2020 Assembly Challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it