DiffusionRL: 通过RL适应的大规模数据集高效训练扩散策略用于机器人抓取
DiffusionRL: Efficient Training of Diffusion Policies for Robotic Grasping Using RL-Adapted Large-Scale Datasets
AI总结：
本文提出通过RL优化训练扩散策略，利用大规模预建数据集提升机器人灵巧抓取任务的性能，实现80%的成功率，降低数据收集成本，增强实际应用的泛化能力。
AI中文摘要：
扩散模型已在图像、视频和音频生成领域取得成功。近期研究表明，它们在序列决策和灵巧操作中具有潜力，通过建模复杂动作分布的能力实现。然而，数据限制和场景特定适应仍存在问题。本文通过提出优化方法，利用RL增强的大规模数据集训练扩散策略，采用轻量级扩散策略训练五指机械手的灵巧操作任务，并通过姿态采样算法验证。该流程在三个DexGraspNet对象上实现了80%的成功率。通过消除手动数据收集，我们的方法降低了在机器人中采用扩散模型的门槛，增强了实际应用中的泛化能力和鲁棒性。
英文摘要：
Diffusion models have been successfully applied in areas such as image, video, and audio generation. Recent works show their promise for sequential decision-making and dexterous manipulation, leveraging their ability to model complex action distributions. However, challenges persist due to the data limitations and scenario-specific adaptation needs. In this paper, we address these challenges by proposing an optimized approach to training diffusion policies using large, pre-built datasets that are enhanced using Reinforcement Learning (RL). Our end-to-end pipeline leverages RL-based enhancement of the DexGraspNet dataset, lightweight diffusion policy training on a dexterous manipulation task for a five-fingered robotic hand, and a pose sampling algorithm for validation. The pipeline achieved a high success rate of 80% for three DexGraspNet objects. By eliminating manual data collection, our approach lowers barriers to adopting diffusion models in robotics, enhancing generalization and robustness for real-world applications.
