RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version) (2409.02920v2)

Abstract: 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 LLMs 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 LLMs 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/.

RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins

The paper "RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins" addresses the significant challenge of advancing dual-arm robotic systems in versatile real-world applications. This is accomplished by introducing the RoboTwin benchmark, which integrates both real-world and synthetic data, specifically aimed at enhancing the training and evaluation of dual-arm robots in tool usage and human-robot interaction scenarios. The key contributions and technological innovations presented in this paper have substantial implications for the field of robotics, particularly in areas where precise manipulation and coordination are critical.

Key Contributions and Methodologies

The first major contribution of the paper is the development of the RoboTwin benchmark dataset. This dataset is unique due to its inclusion of both real-world teleoperated data and synthetic data generated from digital twins. The real-world data collection is facilitated by the COBOT Magic platform, which utilizes four AgileX Arms and multiple Intel Realsense D-435 RGBD cameras to capture diverse scenarios involving tool usage and human-robot interactions. This platform supports the acquisition of high-quality annotations and a variety of task examples, thus ensuring robust training and evaluation of robotic models.

Another significant aspect of the paper is the real-to-simulation pipeline that transforms single 2D images into detailed 3D models. This is achieved using Artificial Intelligence Generated Content (AIGC). The pipeline allows for accurate and cost-effective creation of digital twins, which include complex geometries, surface textures, and functional coordinate axes. These detailed 3D models facilitate realistic visualizations and simulations that are essential for robotic manipulation tasks. The method's reliance on a single RGB image reduces the cost and complexity typically associated with high-fidelity sensors.

Furthermore, the paper introduces a novel approach for generating expert-level training data using LLMs. The LLMs are employed to generate task-specific pose sequences and trajectory plans automatically, thereby enriching the dataset with high-quality, scenario-specific data. The integration of LLMs allows for the automation of complex interactive sequences, which significantly enhances the precision and efficiency of task executions. This approach not only accelerates the data generation process but also reduces the dependency on exhaustive human demonstrations.

Experimental Results and Implications

The experimental analysis uses six tasks within the RoboTwin benchmark to evaluate the performance of strategies trained with varying quantities of expert data. The results demonstrate a clear improvement in task success rates with an increasing number of expert demonstrations. For instance, the "Block Hammer Beat" task shows a success rate improvement from 24% with 10 demonstrations to 80% with 50 demonstrations. Similar trends are observed across other tasks such as "Empty Cup Place" and "Dual-Bottles Pick", underscoring the effectiveness of the automatically generated expert data.

The provision of a scalable benchmark and dataset that encompass both synthetic and real-world scenarios fills a critical gap in the field of dual-arm robotics. The emphasis on tool usage and human-robot interaction aligns the benchmark closely with practical applications, enhancing the relevance and applicability of research outcomes. The approach also facilitates comprehensive testing and refinement of robotic algorithms, particularly in dynamic and complex environments.

Future Directions

The innovations introduced in RoboTwin pave the way for several future research directions in AI and robotics. The utilization of LLMs for generating expert data could be further explored and expanded to cover a wider array of tasks and scenarios. Enhancing the fidelity of digital twins and improving the real-to-simulation pipeline are other potential areas of development. Additionally, integrating real-time feedback mechanisms into the benchmark could allow for adaptive learning and real-world performance optimization.

From a theoretical perspective, the techniques for automating expert data generation and task-specific pose sequencing have broad implications for machine learning and AI. These methods could be adapted to other domains where similar challenges of data scarcity and task complexity exist. The successful application of AI-generated content in robotics also invites interdisciplinary research, blending advancements in computer vision, natural language processing, and robotics.

In conclusion, the RoboTwin benchmark represents a significant advancement in the field of dual-arm robotics. By addressing the limitations of existing datasets and introducing innovative methodologies for data generation and simulation, the paper sets a new standard for research and development in robotic manipulation and human-robot interaction. The practical and theoretical implications of this work are poised to accelerate the evolution of more capable and versatile robotic systems, thereby expanding the horizons of autonomous robotic applications in real-world environments.