Comparing Block-Based Programming Models for Two-Armed Robots
Bibliographic record
Abstract
Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically programmed by experts, but there are not enough to meet the growing demand for robots. To reduce the need for experts, researchers have tried to make robot programming accessible to factory workers without programming experience. However, none of that previous work supports coordinating multiple robot arms that work on the same task. In this paper we present four block-based programming language designs that enable end-users to program two-armed robots. We analyze the benefits and trade-offs of each design on expressiveness and user cognition, and evaluate the designs based on a survey of 273 professional participants of whom 110 had no previous programming experience. We further present an interactive experiment based on a prototype implementation of the design we deem best. This experiment confirmed that novices can successfully use our prototype to complete realistic robotics tasks. This work contributes to making coordinated programming of robots accessible to end-users. It further explores how visual programming elements can make traditionally challenging programming tasks more beginner-friendly.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".