Multi-scale assembly with robot teams
Bibliographic record
Abstract
In this paper we present algorithms and experiments for multi-scale assembly of complex structures by multi-robot teams. We also focus on tasks where successful completion requires multiple types of assembly operations with a range of precision requirements. We develop a hierarchical planning approach to multi-scale perception in support of multi-scale manipulation, in which the resolution of the perception operation is matched with the required resolution for the manipulation operation. We demonstrate these techniques in the context of a multi-step task where robots assemble large box-like objects, inspired by the assembly of an airplane wing. The robots begin by transporting a wing panel, a coarse manipulation operation that requires a wide field of view, and gradually shifts to a narrower field of view but with more accurate sensors for part alignment and fastener insertion. Within this framework we also provide for failure detection and recovery: upon losing track of a feature, the robots retract to using wider field of view systems to re-localize. Finally, we contribute collaborative manipulation algorithms for assembling complex large objects. First, the team of robots coordinates to transport large assembly parts which are too heavy for a single robot to carry. Second, the fasteners and parts are co-localized for robust insertion and fastening. We implement these ideas using four KUKA youBot robots and present experiments where our robots successfully complete all 80 of the attempted fastener insertion operations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".