tional conditions to enhance diversity.We validate the model on both established and newly introduced simulation benchmarks, where it significantly outperforms prior state-of-the-art methods.Furthermore, we demonstrate its effectiveness and robustness through real-world robot experiments.
This paper introduces Dex1B, a large-scale dataset containing one billion demonstrations for dexterous hand manipulation tasks, specifically focusing on grasping and articulation. The authors propose a novel data generation pipeline that integrates optimization techniques with generative models to efficiently produce diverse and high-quality manipulation demonstrations. By incorporating geometric constraints into the generative model, their approach addresses issues of feasibility and diversity, significantly enhancing the performance of state-of-the-art methods. The proposed baseline model, DexSimple, leverages this dataset to achieve superior results across various dexterous manipulation tasks. Extensive validation is carried out through established benchmarks and real-world robot experiments, demonstrating the effectiveness and robustness of both the Dex1B dataset and the DexSimple model.
This paper employs the following methods:
- Generative Model
- Optimization Techniques
- Conditional Variational Autoencoder (CVAE)
- Debiasing Mechanism
The following datasets were used in this research:
- Success Rate
- Q1-score
- Penetration
- H mean
- H std
- Dex1B comprises 1 billion diverse demonstrations for dexterous manipulation tasks, significantly improving upon previous datasets in scale and diversity.
- DexSimple achieves state-of-the-art performance across dexterous manipulation tasks, showing a 22-point improvement over previous methods on the DexGraspNet benchmark.
The authors identified the following limitations:
- The method operates in an open-loop manner, making it prone to sim-to-real gaps and control errors.
- The simulation filtering process for successful data is time-consuming and could be optimized further.
- The approach mainly focuses on single-object scenarios, potentially requiring a stronger vision backbone for multi-object settings.
- Number of GPUs: 1
- GPU Type: RTX-3090
- Compute Requirements: 2000 grasps for 6000 steps on a single GPU