RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
Not on the map. Why?
Cited by 39 in Vision-language-action models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 121 papers in the map (data through 14 Oct 2026). Builds on 65 of its 74 references that are in the map. First posted on arXiv 17 Apr 2025.
Builds on
65 of 74 references.
References · 65
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50 most cited of 65 All 65 (JSON)
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Citing papers per year: 2025: 37; 2026: 84.
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- 2026VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control17 Sep 2026
- 2026Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands14 Sep 2026
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- 2026Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning21 Aug 2026
- 2026Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels28 Jul 2026
- 2026GeoProp: Grounding Robot State in Vision for Generalist Manipulation8 Jul 2026
- 2026Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy6 Jul 2026
- 2026RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies5 Jul 2026
- 2026SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)5 Jul 2026
- 2026VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes29 Jun 2026
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