# Multiview Progress Prediction of Robot Activities

arXiv: https://arxiv.org/abs/2603.00151 · HTML: https://mlsanity.com/paper/2603.00151/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Temporal Cycle-Consistency Learning](https://mlsanity.com/paper/1904.07846/) (2019)
- [RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations](https://mlsanity.com/paper/1802.03243/) (2018)
- [CataNet: Predicting remaining cataract surgery duration](https://mlsanity.com/paper/2106.11048/) (2021)
- [LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection](https://mlsanity.com/paper/2011.07915/) (2020)
- [Am I Done? Predicting Action Progress in Videos](https://mlsanity.com/paper/1705.01781/) (2017)
- [Human Action Forecasting by Learning Task Grammars](https://mlsanity.com/paper/1709.06391/) (2017)
- [Progress Estimation and Phase Detection for Sequential Processes](https://mlsanity.com/paper/1702.08623/) (2017)
- [Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos](https://mlsanity.com/paper/1903.00304/) (2019)
- [Is there progress in activity progress prediction?](https://mlsanity.com/paper/2308.05533/) (2023)

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

- [StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation](https://mlsanity.com/paper/2609.20791/) (2026)

## Abstract (arXiv)

For robots to operate effectively and safely alongside humans, they must be able to understand the progress of ongoing actions. This ability, known as action progress prediction, is critical for tasks ranging from timely assistance to autonomous decision-making. However, modeling action progression in robotics has often been overlooked. Moreover, a single camera may be insufficient for understanding robot's ego-actions, as self-occlusion can significantly hinder perception and model performance. In this paper, we propose a multi-view architecture for action progress prediction in robot manipulation tasks. Experiments on Mobile ALOHA demonstrate the effectiveness of the proposed approach.