Papers for

industrial assembly teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Predictive feedback reduces errors in free-choice assembly tasks

TanCRAFT: Context-Reasoned Anticipatory Feedback for Assembly Tasks

Abstract: Preventing human error in physical assembly tasks requires assistance systems that can intervene before mistakes are made. This is especially challenging in settings where users are free to choose the construction order and correctness depends on the evolving assembly rather than a fixed next step. We present TanCRAFT, a four-stage real-time predictive assistance system that tracks the scene state, reasons about the current partial assembly, forecasts the near-future motion of manipulated pieces, and intervenes when the predicted placement is incompatible with the target. We instantiate our framework and confirm its effectiveness in a controlled tangram assembly task through offline benchmarks and a within-subject user study. Our findings show that the predictive feedback significantly reduces mistakes relative to unguided and reactive sessions, while yielding the lowest mean perceived workload and shortest mean completion time. Overall, these results demonstrate the value of predictive feedback for error prevention and provide a step toward cost-aware assistance for automation and human-robot workcells.

Tue 10 NovRoboticsComputer Vision and Pattern Recognition
The gist
People can make mistakes when assembling things by hand, especially when they choose the order of steps themselves. The authors made a system called TanCRAFT that watches what you’re building, guesses what piece you’ll put where next, and warns you if it sees a mistake about to happen. They tested it on a puzzle-like task and found that it helped people make fewer mistakes, finish faster, and feel less stressed compared to no help or only reacting after a mistake. This shows that predicting errors before they happen can improve assembly work.
Open → 2611.14470v1

Hand motion model improves anomaly detection in two-handed assembly

Hand-Aware Transition Modeling for Bimanual Procedural Anomaly Detection

Abstract: Procedural anomaly detection in bimanual assembly requires judging each hand action against the execution so far. A corrective action may look unusual in isolation, while a visually plausible action can violate the order of the procedure. We present HACT, a transition model over predicted per-hand events. A role-preserving history keeps the concurrent responsibilities of both hands, and a marked temporal point process assigns each observed transition a semantic and temporal surprisal. A supervised evidence head and a two-state filter convert these surprisals into per-hand anomaly posteriors. A recovery-aware protocol on predicted events and participant-disjoint folds reports the recovery false-positive rate at an operating point selected on validation participants. On two bimanual power-tool procedures HACT has the highest AUPRC and F1 among the compared methods and the fewest recovery alarms. Applied without retraining to a different assembly order of the same product, it retains the highest AUPRC and F1. The source code is available at https://github.com/Kratos-Wen/HACT.

Fri 18 SeptComputer Vision and Pattern Recognition
The gist
Detecting mistakes during two-handed assembly tasks is tricky because some fixes might look strange alone, while other actions seem normal but break the work order. The authors created a method called HACT that watches each hand’s moves and learns what’s expected over time. It spots unexpected actions by comparing them to both their meaning and timing within the task. Tested on different power-tool assembly tasks, HACT detected errors more accurately than other techniques and worked well even when task order changed.
Open → 2609.21207v1