提出可实时执行的灵巧手动作重定向框架，兼顾高频率与安全性。
Kilohertz-Safe: A Scalable Framework for Constrained Dexterous Retargeting
- 将非线性重定向问题转为关节微分空间的凸二次规划
- 平均延迟9.05毫秒，超95%动作满足安全约束
- 适合需要高频控制与安全保证的机器人操作场景
灵巧手遥操作需要同时实现高频率实时性能和异构运动学与安全约束的强制执行。现有基于非线性优化的方法通常计算成本过高，难以应用于千赫级控制；而基于学习的方法通常缺乏形式化安全保证。本文提出一种可扩展的动作重定向框架，将非线性重定向问题重构为关节微分空间中的凸二次规划。通过系统线化处理运动学限制与碰撞规避等异构约束，提升计算效率与数值稳定性。进一步引入控制屏障函数，提供重定向过程中的形式化安全保证。在Wuji Hand平台的仿真与硬件实验中验证，该框架优于Dex-Retargeting和GeoRT等先进方法，实现平均9.05毫秒延迟，超过95%的重定向帧满足安全标准，有效缓解复杂操作任务中的自碰撞与穿透问题。
原文摘要 · Abstract (English)
Dexterous hand teleoperation requires motion re-targeting methods that simultaneously achieve high-frequency real-time performance and enforcement of heterogeneous kinematic and safety constraints. Existing nonlinear optimization-based approaches often incur prohibitive computational cost, limiting their applicability to kilohertz-level control, while learning-based methods typically lack formal safety guarantees. This paper proposes a scalable motion retargeting framework that reformulates the nonlinear retargeting problem into a convex quadratic program in joint differential space. Heterogeneous constraints, including kinematic limits and collision avoidance, are incorporated through systematic linearization, resulting in improved computational efficiency and numerical stability. Control barrier functions are further integrated to provide formal safety guarantees during the retargeting process. The proposed framework is validated through simulations and hardware experiments on the Wuji Hand platform, outperforming state-of-the-art methods such as Dex-Retargeting and GeoRT. The framework achieves high-frequency operation with an average latency of 9.05 ms, while over 95% of retargeted frames satisfy the safety criteria, effectively mitigating self-collision and penetration during complex manipulation tasks.
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