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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.21161 |
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| _version_ | 1866910041333825536 |
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| author | Hu, Xu Feng, Yiyang Peng, Junran He, Jiawei Chen, Liyi Sui, Wei Luo, Chuanchen Yin, Xucheng Li, Qing Zhang, Zhaoxiang |
| author_facet | Hu, Xu Feng, Yiyang Peng, Junran He, Jiawei Chen, Liyi Sui, Wei Luo, Chuanchen Yin, Xucheng Li, Qing Zhang, Zhaoxiang |
| contents | The development of embodied agents for complex commercial environments is hindered by a critical gap in existing robotics datasets and benchmarks, which primarily focus on household or tabletop settings with short-horizon tasks. To address this limitation, we introduce MarketGen, a scalable simulation platform with automatic scene generation for complex supermarket environments. MarketGen features a novel agent-based Procedural Content Generation (PCG) framework. It uniquely supports multi-modal inputs (text and reference images) and integrates real-world design principles to automatically generate complete, structured, and realistic supermarkets. We also provide an extensive and diverse 3D asset library with a total of 1100+ supermarket goods and parameterized facilities assets. Building on this generative foundation, we propose a novel benchmark for assessing supermarket agents, featuring two daily tasks in a supermarket: (1) Checkout Unloading: long-horizon tabletop tasks for cashier agents, and (2) In-Aisle Item Collection: complex mobile manipulation tasks for salesperson agents. We validate our platform and benchmark through extensive experiments, including the deployment of a modular agent system and successful sim-to-real transfer. MarketGen provides a comprehensive framework to accelerate research in embodied AI for complex commercial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MarketGen: A Scalable Simulation Platform with Auto-Generated Embodied Supermarket Environments Hu, Xu Feng, Yiyang Peng, Junran He, Jiawei Chen, Liyi Sui, Wei Luo, Chuanchen Yin, Xucheng Li, Qing Zhang, Zhaoxiang Robotics The development of embodied agents for complex commercial environments is hindered by a critical gap in existing robotics datasets and benchmarks, which primarily focus on household or tabletop settings with short-horizon tasks. To address this limitation, we introduce MarketGen, a scalable simulation platform with automatic scene generation for complex supermarket environments. MarketGen features a novel agent-based Procedural Content Generation (PCG) framework. It uniquely supports multi-modal inputs (text and reference images) and integrates real-world design principles to automatically generate complete, structured, and realistic supermarkets. We also provide an extensive and diverse 3D asset library with a total of 1100+ supermarket goods and parameterized facilities assets. Building on this generative foundation, we propose a novel benchmark for assessing supermarket agents, featuring two daily tasks in a supermarket: (1) Checkout Unloading: long-horizon tabletop tasks for cashier agents, and (2) In-Aisle Item Collection: complex mobile manipulation tasks for salesperson agents. We validate our platform and benchmark through extensive experiments, including the deployment of a modular agent system and successful sim-to-real transfer. MarketGen provides a comprehensive framework to accelerate research in embodied AI for complex commercial applications. |
| title | MarketGen: A Scalable Simulation Platform with Auto-Generated Embodied Supermarket Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.21161 |