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Kilohertz-Safe: A Scalable Framework for Constrained Dexterous Retargeting

Yinxiao Tian, Ziyi Yang, Zinan Zhao, Zhen Kan

Abstract

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.

Kilohertz-Safe: A Scalable Framework for Constrained Dexterous Retargeting

Abstract

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.

Paper Structure

This paper contains 15 sections, 21 equations, 8 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Preliminaries
  3. Dexterous Hand Retargeting
  4. Control Barrier Functions
  5. Method
  6. Quadratic Programming Formulation of Hand Pose Retargeting
  7. Control Barrier Function–Based Safety Guarantee
  8. Experiment
  9. Experimental Setup
  10. Simulation Evaluation
  11. Computation Latency
  12. Motion Preservation
  13. Safety Analysis
  14. Real-World Evaluation
  15. CONCLUSIONS

Figures (8)

  • Figure 3: System pipeline of the proposed high-frequency retargeting framework. High-frequency human inputs and robot state feedback are unified through velocity-level constraint linearization, forming a structured convex optimization problem that enables stable real-time retargeting under geometric, kinematic, and safety constraints.
  • Figure 4: Motion preservation comparison across retargeting pipelines. (a) Global motion error over the entire interaction sequence. Ours and Dex-Retargeting are plotted against the left vertical axis, whereas GeoRT is shown using the right vertical axis due to its distinct numerical scale. Dual axes are adopted solely for visualization clarity. (b) Cumulative motion preservation advantage of our method compared with Dex-Retargeting. (c–d) Zoomed-in views of critical interaction stages, highlighting fine-grained finger posture preservation. (e) Efficiency–fidelity trade-off: mean motion preservation, averaged over all frames in the interaction sequence, versus total computation time accumulated over the same sequence for Ours, Dex-Retargeting, and GeoRT. (f) Segment-wise comparison (50-frame bins) of mean motion preservation and mean computation time: bars for motion preservation, markers for latency.
  • Figure 5: Qualitative results illustrating robust human-to-robot hand retargeting across diverse hand gestures.
  • Figure 6: Analysis of Safety Score Across Different Retargeting Pipelines. Annotated stages are defined based on the human hand motion and are shared across all methods.
  • Figure 7: CBF ablation study comparing the safety score and minimum inter-finger distance with and without CBF constraints. The CBF-constrained method maintains consistently higher safety levels, with only minor millimeter-scale threshold violations in a few frames due to discrete-time control and system latency.
  • ...and 3 more figures

Theorems & Definitions (1)

  • Remark 1