Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.00400 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911087058747392 |
|---|---|
| author | Gajo, Janika Deborah Merales, Gerarld Paul Escarcha, Jerome Molina, Brenden Ashley Nartea, Gian Maminta, Emmanuel G. Roldan, Juan Carlos Atienza, Rowel O. |
| author_facet | Gajo, Janika Deborah Merales, Gerarld Paul Escarcha, Jerome Molina, Brenden Ashley Nartea, Gian Maminta, Emmanuel G. Roldan, Juan Carlos Atienza, Rowel O. |
| contents | We present Sari Sandbox, a high-fidelity, photorealistic 3D retail store simulation for benchmarking embodied agents against human performance in shopping tasks. Addressing a gap in retail-specific sim environments for embodied agent training, Sari Sandbox features over 250 interactive grocery items across three store configurations, controlled via an API. It supports both virtual reality (VR) for human interaction and a vision language model (VLM)-powered embodied agent. We also introduce SariBench, a dataset of annotated human demonstrations across varied task difficulties. Our sandbox enables embodied agents to navigate, inspect, and manipulate retail items, providing baselines against human performance. We conclude with benchmarks, performance analysis, and recommendations for enhancing realism and scalability. The source code can be accessed via https://github.com/upeee/sari-sandbox-env. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sari Sandbox: A Virtual Retail Store Environment for Embodied AI Agents Gajo, Janika Deborah Merales, Gerarld Paul Escarcha, Jerome Molina, Brenden Ashley Nartea, Gian Maminta, Emmanuel G. Roldan, Juan Carlos Atienza, Rowel O. Computer Vision and Pattern Recognition We present Sari Sandbox, a high-fidelity, photorealistic 3D retail store simulation for benchmarking embodied agents against human performance in shopping tasks. Addressing a gap in retail-specific sim environments for embodied agent training, Sari Sandbox features over 250 interactive grocery items across three store configurations, controlled via an API. It supports both virtual reality (VR) for human interaction and a vision language model (VLM)-powered embodied agent. We also introduce SariBench, a dataset of annotated human demonstrations across varied task difficulties. Our sandbox enables embodied agents to navigate, inspect, and manipulate retail items, providing baselines against human performance. We conclude with benchmarks, performance analysis, and recommendations for enhancing realism and scalability. The source code can be accessed via https://github.com/upeee/sari-sandbox-env. |
| title | Sari Sandbox: A Virtual Retail Store Environment for Embodied AI Agents |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.00400 |