RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain
  Randomization for Robust Bimanual Robotic Manipulation
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.
685ae8e8d2ee4fac76521d03
Tianxing Chen
claimed_verified
2025-06-24T18:13:30.491Z
65b37a9b06d8b55123ef8921
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Tianxing Chen
user
TianxingChen
685ae8e8d2ee4fac76521d04
Zanxin Chen
685ae8e8d2ee4fac76521d05
Baijun Chen
685ae8e8d2ee4fac76521d06
Zijian Cai
685ae8e8d2ee4fac76521d07
Yibin Liu
685ae8e8d2ee4fac76521d08
Qiwei Liang
685ae8e8d2ee4fac76521d09
Zixuan Li
685ae8e8d2ee4fac76521d0a
Xianliang Lin
685ae8e8d2ee4fac76521d0b
Yiheng Ge
685ae8e8d2ee4fac76521d0c
Zhenyu Gu
685ae8e8d2ee4fac76521d0d
Weiliang Deng
685ae8e8d2ee4fac76521d0e
Yubin Guo
685ae8e8d2ee4fac76521d0f
Tian Nian
685ae8e8d2ee4fac76521d10
Xuanbing Xie
685ae8e8d2ee4fac76521d11
Qiangyu Chen
685ae8e8d2ee4fac76521d12
Kailun Su
685ae8e8d2ee4fac76521d13
Tianling Xu
685ae8e8d2ee4fac76521d14
Guodong Liu
685ae8e8d2ee4fac76521d15
Mengkang Hu
685ae8e8d2ee4fac76521d16
Huan-ang Gao
685ae8e8d2ee4fac76521d17
Kaixuan Wang
685ae8e8d2ee4fac76521d18
Zhixuan Liang
685ae8e8d2ee4fac76521d19
Yusen Qin
685ae8e8d2ee4fac76521d1a
Xiaokang Yang
685ae8e8d2ee4fac76521d1b
Ping Luo
685ae8e8d2ee4fac76521d1c
Yao Mu
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain
  Randomization for Robust Bimanual Robotic Manipulation
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.
RoboTwin 2.0 is a scalable simulation framework for bimanual robotic manipulation that uses expert data synthesis and structured domain randomization to generate diverse and realistic synthetic data, improving sim-to-real transfer and generalization.
multimodal large language models simulation-in-the-loop structured domain randomization bimanual manipulation task-level execution code sim-to-real transfer zero-shot learning