MarketGen: A Scalable Simulation Platform with Auto-Generated Embodied Supermarket Environments
Xu Hu, Yiyang Feng, Junran Peng, Jiawei He, Liyi Chen, Wei Sui, Chuanchen Luo, Xucheng Yin, Qing Li, Zhaoxiang Zhang

TL;DR
MarketGen is a scalable simulation platform that automatically generates realistic supermarket environments, enabling research on embodied AI agents for complex commercial tasks with diverse assets and benchmarks.
Contribution
It introduces a novel agent-based procedural scene generation framework supporting multi-modal inputs and a new benchmark for supermarket tasks.
Findings
Successful sim-to-real transfer of agents
Extensive dataset with 1100+ supermarket goods
Validation of platform through diverse experiments
Abstract
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,…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Social Robot Interaction and HRI · Generative Adversarial Networks and Image Synthesis
