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
Published: 2025-06-22 16:26:53

Abstract: 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.