RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain
  Randomization for Robust Bimanual Robotic Manipulation
Authors: name: Tianxing Chen, name: Zanxin Chen, name: Baijun Chen, name: Zijian Cai, name: Yibin Liu, name: Qiwei Liang, name: Zixuan Li, name: Xianliang Lin, name: Yiheng Ge, name: Zhenyu Gu, name: Weiliang Deng, name: Yubin Guo, name: Tian Nian, name: Xuanbing Xie, name: Qiangyu Chen, name: Kailun Su, name: Tianling Xu, name: Guodong Liu, name: Mengkang Hu, name: Huan-ang Gao, name: Kaixuan Wang, name: Zhixuan Liang, name: Yusen Qin, name: Xiaokang Yang, name: Ping Luo, name: Yao Mu

Simulation-based data synthesis has emerged as a powerful paradigm for
enhancing real-world robotic manipulation. However, existing synthetic datasets
remain insufficient for robust bimanual manipulation due to two challenges: (1)
the lack of an efficient, scalable data generation method for novel tasks, and
(2) oversimplified simulation environments that fail to capture real-world
complexity. We present RoboTwin 2.0, a scalable simulation framework that
enables automated, large-scale generation of diverse and realistic data, along
with unified evaluation protocols for dual-arm manipulation. We first construct
RoboTwin-OD, a large-scale object library comprising 731 instances across 147
categories, each annotated with semantic and manipulation-relevant labels.
Building on this foundation, we develop an expert data synthesis pipeline that
combines multimodal large language models (MLLMs) with simulation-in-the-loop
refinement to generate task-level execution code automatically. To improve
sim-to-real transfer, RoboTwin 2.0 incorporates structured domain randomization
along five axes: clutter, lighting, background, tabletop height and language
instructions, thereby enhancing data diversity and policy robustness. We
instantiate this framework across 50 dual-arm tasks spanning five robot
embodiments, and pre-collect over 100,000 domain-randomized expert
trajectories. Empirical results show a 10.9% gain in code generation success
and improved generalization to novel real-world scenarios. A VLA model
fine-tuned on our dataset achieves a 367% relative improvement (42.0% vs. 9.0%)
on unseen scene real-world tasks, while zero-shot models trained solely on our
synthetic data achieve a 228% relative gain, highlighting strong generalization
without real-world supervision. We release the data generator, benchmark,
dataset, and code to support scalable research in robust bimanual manipulation.