用少量示范生成高质量数据，提升机械手操作精度与泛化能力。
FAR-Dex: Few-shot Data Augmentation and Adaptive Residual Policy Refinement for Dexterous Manipulation
- 通过仿真生成多样动作轨迹，弥补示范数据不足
- 残差模块融合多步轨迹与观测信息，提升操作准确率
- 实测成功率超80%，适合复杂精细操作任务
实现人手级灵巧操作仍面临高维动作空间与高质量示范数据稀缺的挑战。为此，我们提出FAR-Dex框架，结合少样本数据增强与自适应残差策略优化，实现臂手协同的鲁棒精准控制。首先，FAR-DexGen利用IsaacLab仿真器从少数示范中生成多样化且物理合理的轨迹，为策略训练提供数据基础；其次，FAR-DexRes引入自适应残差模块，将多步轨迹片段与观测特征融合，提升策略在复杂操作场景中的精度与鲁棒性。仿真与真实世界实验表明，FAR-Dex相较现有最优方法，数据质量提升13.4%，任务成功率提高7%；在真实场景中成功率达80%以上，实现高精度灵巧操作与强位置泛化能力。
原文摘要 · Abstract (English)
Achieving human-like dexterous manipulation through the collaboration of multi-fingered hands with robotic arms remains a longstanding challenge in robotics, primarily due to the scarcity of high-quality demonstrations and the complexity of high-dimensional action spaces. To address these challenges, we propose FAR-Dex, a hierarchical framework that integrates few-shot data augmentation with adaptive residual refinement to enable robust and precise arm-hand coordination in dexterous tasks. First, FAR-DexGen leverages the IsaacLab simulator to generate diverse and physically constrained trajectories from a few demonstrations, providing a data foundation for policy training. Second, FAR-DexRes introduces an adaptive residual module that refines policies by combining multi-step trajectory segments with observation features, thereby enhancing accuracy and robustness in manipulation scenarios. Experiments in both simulation and real-world demonstrate that FAR-Dex improves data quality by 13.4% and task success rates by 7% over state-of-the-art methods. It further achieves over 80% success in real-world tasks, enabling fine-grained dexterous manipulation with strong positional generalization.
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