Kilohertz-Safe: A Scalable Framework for Constrained Dexterous Retargeting
This addresses the challenge of safe and efficient teleoperation for robotic hands, though it is incremental by building on existing optimization and learning methods with improved computational efficiency and formal safety integration.
The paper tackled the problem of achieving high-frequency real-time performance with safety guarantees in dexterous hand teleoperation by proposing a scalable motion retargeting framework that reformulates the problem into a convex quadratic program. It resulted in an average latency of 9.05 ms and over 95% of retargeted frames satisfying safety criteria, outperforming state-of-the-art methods.
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.