用安全约束函数让人形机器人在扰动下仍能保持关节、避障等安全约束。
Safety-Critical Whole-Body Control for Humanoid Robots via Input-to-State Safe Control Barrier Functions
- 通过输入到状态安全屏障函数，动态修正运动指令以保证安全。
- 在模型不准确和外部干扰下，仍能实时满足多类安全约束。
- 适合需要高安全性的实机操作场景，如行走、单腿平衡与远程控制。
人形机器人在复杂人机共存环境中运行时，必须满足关节限位、自碰撞规避、障碍物避让和工作空间边界等物理安全约束。然而，现有方法在存在未知扰动（如模型误差、轨迹跟踪偏差和外部干扰）时，难以保证运动学安全。本文提出一种基于输入到状态安全控制屏障函数（ISSf-CBFs）的分层安全关键全身控制框架。该架构包含运动学级全身控制器（KinWBC）、ISSf-CBF安全滤波器和动力学级全身控制器（DynWBC）。KinWBC生成优先级任务下的名义关节运动参考；ISSf-CBF滤波器在有界扰动下最小化修正这些参考以满足运动学安全；DynWBC则跟踪经滤波的参考，同时确保全身体动力学可行性和接触稳定性。安全约束作用于全身运动学模型，且通过保守调参，使运动学安全可传递至高阶人形动力学系统。仿真与真实机器人实验表明，该框架在模型失配条件下显著提升安全裕度，实时可靠地实现多类安全约束，适用于步态行走、远程操控及单腿平衡带手控等场景。
原文摘要 · Abstract (English)
Safety-critical control is essential for humanoid robots operating in complex human-centered environments, where physical safety constraints such as joint limits, self-collision avoidance, obstacle avoidance, and workspace boundaries must be satisfied during real-robot operation. However, existing approaches remain limited because kinematic safety guarantees can be degraded in the presence of unknown disturbances, such as model uncertainties, trajectory-tracking errors, and external perturbations. This paper presents a hierarchical safety-critical whole-body control framework for humanoid robots based on input-to-state safe control barrier functions (ISSf-CBFs). The proposed architecture integrates a kinematic-level whole-body controller (KinWBC), an ISSf-CBF safety filter, and a dynamic-level whole-body controller (DynWBC). KinWBC generates nominal joint-motion references from prioritized tasks; the ISSf-CBF filter minimally modifies these references to satisfy kinematic safety constraints under bounded disturbances; and DynWBC tracks the filtered references while enforcing full-body dynamic feasibility and contact stability. Safety constraints are imposed on a whole-body kinematic model, and the ISSf-CBF parameters are conservatively tuned so that the resulting kinematic safety guarantees can be transferred to full-order humanoid dynamics under unknown disturbances. Simulation and real-robot experiments demonstrate that the proposed framework improves safety margins under model mismatch and reliably enforces multiple safety constraints in real time during locomotion, teleoperation, and single-leg balancing with hand control. Project website: https://kwlee365.github.io/SafeWBC-Website/
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