Fault tolerant robot programming by demonstration of sorting tasks with industrial objects
Bibliographic record
Abstract
The goal of programming by demonstration (PBD) (also known as “learning by demonstration” and “imitation learning”) is for the robot to learn its program from a human demonstrator or teacher, saving time and money compared with traditional robot programming. This paper focuses on programming robots using human pointing gestures to automatically sort objects into bins. The proposed PBD system's software design, algorithms and experimental implementation are presented. Gesturing, speech and graphics facilitate the human-robot interaction. The main novelty of the system is its ability to tolerate human and robot faults. The tolerated human faults include: vague pointing gesture, timeout during pointing, pointing to previously matched class, pointing to previously matched bin, unclassified object found, matching bin not found, and human inside work zone during sorting task. Dropping an object during pick-and-place is the tolerated robot fault. The hardware includes a single color plus depth camera, and a six- axis robotic arm with an electromagnetic gripper. The software runs on a standard PC. The system's ability to deal with human and robot faults was verified using teaching and sorting experiments performed with a set of industrial parts.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".