# Optimizing Multitask Industrial Processes with Predictive Action Guidance

arXiv: https://arxiv.org/abs/2501.05108 · HTML: https://mlsanity.com/paper/2501.05108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition](https://mlsanity.com/paper/2201.08383/) (2022)
- [Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video](https://mlsanity.com/paper/2005.02190/) (2020)
- [What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention](https://mlsanity.com/paper/1905.09035/) (2019)
- [The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain](https://mlsanity.com/paper/2010.05654/) (2020)
- [Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture](https://mlsanity.com/paper/1509.05016/) (2015)
- [Intention-Conditioned Long-Term Human Egocentric Action Forecasting](https://mlsanity.com/paper/2207.12080/) (2022)
- [Self-Regulated Learning for Egocentric Video Activity Anticipation](https://mlsanity.com/paper/2111.11631/) (2021)
- [Uncertainty-boosted Robust Video Activity Anticipation](https://mlsanity.com/paper/2404.18648/) (2024)
- [Streaming egocentric action anticipation: An evaluation scheme and approach](https://mlsanity.com/paper/2306.16682/) (2023)
- [Human-in-the-loop Robotic Manipulation Planning for Collaborative Assembly](https://mlsanity.com/paper/1909.11280/) (2019)
- [Vision- and tactile-based continuous multimodal intention and attention recognition for safer physical human-robot interaction](https://mlsanity.com/paper/2206.11350/) (2022)

## Built on by (most cited first, 1 of 1)

- [G3Ego: Gaze-Guided Graphs for Egocentric Action Understanding](https://mlsanity.com/paper/2608.20157/) (2026)

## Abstract (arXiv)

Monitoring complex assembly processes is critical for maintaining productivity and ensuring compliance with assembly standards. However, variability in human actions and subjective task preferences complicate accurate task anticipation and guidance. To address these challenges, we introduce the Multi-Modal Transformer Fusion and Recurrent Units (MMTFRU) Network for egocentric activity anticipation, utilizing multimodal fusion to improve prediction accuracy. Integrated with the Operator Action Monitoring Unit (OAMU), the system provides proactive operator guidance, preventing deviations in the assembly process. OAMU employs two strategies: (1) Top-5 MMTF-RU predictions, combined with a reference graph and an action dictionary, for next-step recommendations; and (2) Top-1 MMTF-RU predictions, integrated with a reference graph, for detecting sequence deviations and predicting anomaly scores via an entropy-informed confidence mechanism. We also introduce Time-Weighted Sequence Accuracy (TWSA) to evaluate operator efficiency and ensure timely task completion. Our approach is validated on the industrial Meccano dataset and the largescale EPIC-Kitchens-55 dataset, demonstrating its effectiveness in dynamic environments.