基于渐进式运动学-动力学对齐的灵巧操作迁移
Dexterous Manipulation Transfer via Progressive Kinematic-Dynamic Alignment
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中文总结 AI 辅助
针对多指灵巧手操作数据稀缺问题，提出渐进式运动学-动力学对齐的手型无关迁移框架，可将人手演示视频转换为高质量灵巧操作轨迹，平均迁移成功率达73%，具备强泛化性与可扩展性。
中文摘要 AI 辅助
使用多指机器人手硬件平台采集操作数据存在固有难度且可扩展性有限，造成了严重的数据稀缺问题，阻碍了数据驱动的灵巧操作策略学习研究。为应对这一挑战，我们提出了一种与手型无关的操作迁移系统，该系统无需海量训练数据，即可将演示视频中的人手操作序列高效转换为高质量的灵巧操作轨迹。为解决人手与灵巧手之间的多维度差异，以及灵巧手高自由度协同控制带来的挑战，我们设计了渐进式迁移框架：首先基于运动学匹配为灵巧手建立基础控制信号；随后训练带有动作空间重缩放和拇指引导初始化的残差策略，在统一奖励下动态优化接触交互；最后以保留操作语义为目标计算腕部控制轨迹。仅使用人手操作视频，我们的系统即可针对不同任务自动配置系统参数，在不同灵巧手、物体类别和任务间平衡运动学匹配与动力学优化。大量实验结果表明，该框架能够自动生成流畅且语义正确的灵巧手操作，忠实复现人类意图，平均迁移成功率达73%，具备高效率与强泛化性，为机器人灵巧操作数据采集提供了易实现、可扩展的方法。
英文摘要
The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy learning. To address this challenge, we present a hand-agnostic manipulation transfer system. It efficiently converts human hand manipulation sequences from demonstration videos into high-quality dexterous manipulation trajectories without requirements of massive training data. To tackle the multi-dimensional disparities between human hands and dexterous hands, as well as the challenges posed by high-degree-of-freedom coordinated control of dexterous hands, we design a progressive transfer framework: first, we establish primary control signals for dexterous hands based on kinematic matching; subsequently, we train residual policies with action space rescaling and thumb-guided initialization to dynamically optimize contact interactions under unified rewards; finally, we compute wrist control trajectories with the objective of preserving operational semantics. Using only human hand manipulation videos, our system automatically configures system parameters for different tasks, balancing kinematic matching and dynamic optimization across dexterous hands, object categories, and tasks. Extensive experimental results demonstrate that our framework can automatically generate smooth and semantically correct dexterous hand manipulation that faithfully reproduces human intentions, achieving high efficiency and strong generalizability with an average transfer success rate of 73%, providing an easily implementable and scalable method for collecting robot dexterous manipulation data.
