RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins

• URL: http://arxiv.org/abs/2504.13059v1
• Date: Thu, 17 Apr 2025 16:14:24 GMT
• Title: RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
• Authors: Yao Mu, Tianxing Chen, Zanxin Chen, Shijia Peng, Zhiqian Lan, Zeyu Gao, Zhixuan Liang, Qiaojun Yu, Yude Zou, Mingkun Xu, Lunkai Lin, Zhiqiang Xie, Mingyu Ding, Ping Luo,
• Abstract summary: RoboTwin is a generative digital twin framework that uses 3D generative foundation models and large language models to produce diverse expert datasets.<n>Specifically, RoboTwin creates varied digital twins of objects from single 2D images, generating realistic and interactive scenarios.<n>Our framework offers a comprehensive benchmark with both simulated and real-world data, enabling standardized evaluation and better alignment between simulated training and real-world performance.
• Score: 33.78621017138685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced autonomous systems. However, the scarcity of diverse, high-quality demonstration data and real-world-aligned evaluation benchmarks severely limits such development. To address this, we introduce RoboTwin, a generative digital twin framework that uses 3D generative foundation models and large language models to produce diverse expert datasets and provide a real-world-aligned evaluation platform for dual-arm robotic tasks. Specifically, RoboTwin creates varied digital twins of objects from single 2D images, generating realistic and interactive scenarios. It also introduces a spatial relation-aware code generation framework that combines object annotations with large language models to break down tasks, determine spatial constraints, and generate precise robotic movement code. Our framework offers a comprehensive benchmark with both simulated and real-world data, enabling standardized evaluation and better alignment between simulated training and real-world performance. We validated our approach using the open-source COBOT Magic Robot platform. Policies pre-trained on RoboTwin-generated data and fine-tuned with limited real-world samples demonstrate significant potential for enhancing dual-arm robotic manipulation systems by improving success rates by over 70% for single-arm tasks and over 40% for dual-arm tasks compared to models trained solely on real-world data.

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