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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.02560 |
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| _version_ | 1866909887068372992 |
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| author | Bohus, Dan Andrist, Sean Paradiso, Ann Saw, Nick Schoonbeek, Tim Stiber, Maia |
| author_facet | Bohus, Dan Andrist, Sean Paradiso, Ann Saw, Nick Schoonbeek, Tim Stiber, Maia |
| contents | We introduce SigmaCollab, a dataset enabling research on physically situated human-AI collaboration. The dataset consists of a set of 85 sessions in which untrained participants were guided by a mixed-reality assistive AI agent in performing procedural tasks in the physical world. SigmaCollab includes a set of rich, multimodal data streams, such as the participant and system audio, egocentric camera views from the head-mounted device, depth maps, head, hand and gaze tracking information, as well as additional annotations performed post-hoc. While the dataset is relatively small in size (~ 14 hours), its application-driven and interactive nature brings to the fore novel research challenges for human-AI collaboration, and provides more realistic testing grounds for various AI models operating in this space. In future work, we plan to use the dataset to construct a set of benchmarks for physically situated collaboration in mixed-reality task assistive scenarios. SigmaCollab is available at https://github.com/microsoft/SigmaCollab. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SigmaCollab: An Application-Driven Dataset for Physically Situated Collaboration Bohus, Dan Andrist, Sean Paradiso, Ann Saw, Nick Schoonbeek, Tim Stiber, Maia Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition We introduce SigmaCollab, a dataset enabling research on physically situated human-AI collaboration. The dataset consists of a set of 85 sessions in which untrained participants were guided by a mixed-reality assistive AI agent in performing procedural tasks in the physical world. SigmaCollab includes a set of rich, multimodal data streams, such as the participant and system audio, egocentric camera views from the head-mounted device, depth maps, head, hand and gaze tracking information, as well as additional annotations performed post-hoc. While the dataset is relatively small in size (~ 14 hours), its application-driven and interactive nature brings to the fore novel research challenges for human-AI collaboration, and provides more realistic testing grounds for various AI models operating in this space. In future work, we plan to use the dataset to construct a set of benchmarks for physically situated collaboration in mixed-reality task assistive scenarios. SigmaCollab is available at https://github.com/microsoft/SigmaCollab. |
| title | SigmaCollab: An Application-Driven Dataset for Physically Situated Collaboration |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.02560 |