Multiview Progress Prediction of Robot Activities
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910035772178432 |
|---|---|
| author | Zoppellari, Elena Becattini, Federico Fiorucci, Marco Ballan, Lamberto |
| author_facet | Zoppellari, Elena Becattini, Federico Fiorucci, Marco Ballan, Lamberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00151 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multiview Progress Prediction of Robot Activities Zoppellari, Elena Becattini, Federico Fiorucci, Marco Ballan, Lamberto Robotics Computer Vision and Pattern Recognition 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. |
| title | Multiview Progress Prediction of Robot Activities |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.00151 |