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：可靠的接触力估计对于确保机器人与非结构化环境之间安全、精确的交互至关重要。然而，由于固有的建模误差以及复杂的残余动力学和摩擦，精确的无传感器力估计仍然具有挑战性。为解决这一问题，本文提出K-VARK（核化方差感知残差卡尔曼滤波器），这是一种将关节残余扭矩的核化概率模型集成到自适应卡尔曼滤波器框架中的新方法。通过基于优化激励轨迹训练的核化运动基元，K-VARK能够捕捉残余扭矩的预测均值以及输入依赖的异方差性，从而反映数据变异性和与训练数据的距离效应。这些统计量通过增强测量噪声协方差来指导方差感知的虚拟测量更新，同时过程噪声协方差通过变分贝叶斯优化在线自适应，以处理动态扰动。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实现了超过20%的均方根误差降低，为抛光和装配等高级任务提供了鲁棒且准确的外部力/扭矩估计。