Reliable estimation of contact forces is crucial for ensuring safe and precise interaction of robots with unstructured environments. However, accurate sensorless force estimation remains challenging due to inherent modeling errors and complex residual dynamics and friction. To address this challenge, in this paper, we propose K-VARK (Kernelized Variance-Aware Residual Kalman filter), a novel approach that integrates a kernelized, probabilistic model of joint residual torques into an adaptive Kalman filter framework. Through Kernelized Movement Primitives trained on optimized excitation trajectories, K-VARK captures both the predictive mean and input-dependent heteroscedastic variance of residual torques, reflecting data variability and distance-to-training effects. These statistics inform a variance-aware virtual measurement update by augmenting the measurement noise covariance, while the process noise covariance adapts online via variational Bayesian optimization to handle dynamic disturbances. Experimental validation on a 6-DoF collaborative manipulator demonstrates that K-VARK achieves over 20% reduction in RMSE compared to state-of-the-art sensorless force estimation methods, yielding robust and accurate external force/torque estimation suitable for advanced tasks such as polishing and assembly.

翻译：接触力的可靠估计对于确保机器人与非结构化环境的安全精确交互至关重要。然而，由于固有的建模误差、复杂的残差动力学及摩擦特性，实现准确的无传感器力估计仍具挑战性。为解决此问题，本文提出K-VARK（核化方差感知残差卡尔曼滤波器）——一种将关节残差扭矩的核化概率模型集成到自适应卡尔曼滤波框架中的新方法。通过基于优化激励轨迹训练的核化运动基元，K-VARK同时捕捉残差扭矩的预测均值与输入依赖的异方差方差，从而反映数据变异性和训练距离效应。这些统计量通过增广测量噪声协方差实现方差感知的虚拟测量更新，同时通过变分贝叶斯优化在线调整过程噪声协方差以应对动态扰动。在六自由度协作机械臂上的实验验证表明，与最先进的无传感器力估计方法相比，K-VARK的均方根误差降低超过20%，可为抛光和装配等高级任务提供鲁棒且准确的外力/扭矩估计。