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RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version)
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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 improve the success rate of over 70% for single-arm tasks and over 40% for dual-arm tasks compared to models trained solely on real-world data. This significant improvement demonstrates RoboTwin's potential to enhance the development and evaluation of dual-arm robotic manipulation systems. Project Page: https://robotwin-benchmark.github.io/early-version/.
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Cited by 12 Pith papers
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ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
ManiFlow trains a flow-matching policy with a continuous-time consistency objective and an adaptive cross-attention transformer, enabling dexterous manipulation with 1-2 inference steps and substantially higher succes...
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DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments
A simulation framework that combines 3D Gaussian Splatting with MuJoCo reports improved zero-shot transfer of manipulation policies from simulation to real robots.
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Diffusion-Based Imaginative Coordination for Bimanual Manipulation
A diffusion-based policy that jointly predicts future video latents and actions improves bimanual manipulation success, with video prediction used only during training.
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TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
A type-guided teleoperation system that selects predefined dexterous hand poses with a language model outperforms retargeting-based teleoperation on nine real-world tasks and improves imitation learning success.
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ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models
ControlVLA adapts a DROID-pretrained diffusion VLA policy to new manipulation tasks with 10 to 20 demos by injecting object-centric features through zero-initialized cross-attention layers, achieving 76.7% success acr...
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BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
BiAssemble predicts bimanual grasp and assembly actions for geometric reassembly of fractured objects via point-level collaborative affordance, and reports simulation gains over baselines plus a real-world benchmark.
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Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction
CogRobot uses optical flow as an intermediate variable to fine-tune a text-to-video model for predicting bimanual robot trajectories, then maps those predictions to actions with a goal-conditioned diffusion policy.
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SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training
Simulation-pretrained policies, with digital-twin demos for critic bootstrapping and action proposals, cut real-world RL training time while reaching near-perfect success on three manipulation tasks.
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AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation
AC-DiT adds mobility-to-body conditioning and perception-aware 2D/3D weighting to a diffusion transformer, improving success rates on simulated and real-world mobile manipulation tasks.
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PRISM: Pointcloud Reintegrated Inference via Segmentation and Cross-attention for Manipulation
PRISM trains a diffusion policy on segmented point-cloud object tokens fused with joint states via cross-attention, reporting 82.0 percent average success across six RoboTwin tasks versus 58.4 percent for DP3 and 22.3...
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Data Pyramid for Embodied Manipulation
Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.
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