Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact-rich scenarios. Traditional approaches often rely on binary contact states that fail to capture the nuances of partial contact or directional slippage. This paper presents CoCo-InEKF, a differentiable invariant extended Kalman filter that utilizes continuous contact velocity covariances instead of binary contact states. These learned covariances allow the method to dynamically modulate contact confidence, accounting for more nuanced conditions ranging from firm contact to directional slippage or no contact. To predict these covariances for a set of predefined contact candidate points, we employ a lightweight neural network trained end-to-end using a state-error loss. This approach eliminates the need for heuristic ground-truth contact labels. In addition, we propose an automated contact candidate selection procedure and demonstrate that our method is insensitive to their exact placement. Experiments on a bipedal robot demonstrate a superior accuracy-efficiency tradeoff for linear velocity estimation, as well as improved filter consistency compared to baseline methods. This enables the robust execution of challenging motions, including dancing and complex ground interactions -- both in simulation and in the real world.

翻译：针对腿式机器人高速动态运动（尤其在动态密集接触场景下）的鲁棒状态估计仍具挑战性。传统方法通常依赖二元接触状态，无法捕捉部分接触或方向性滑移的细微差异。本文提出CoCo-InEKF——一种可微的扩展卡尔曼滤波器，采用连续接触速度协方差替代二元接触状态。这些学习型协方差使方法能动态调节接触置信度，覆盖从稳固接触到方向性滑移或无接触的渐变条件。为预测预定义接触候选点集的协方差，我们采用轻量级神经网络，通过状态误差损失进行端到端训练，从而避免了对启发式真实接触标签的依赖。此外，我们提出自动接触候选点选择流程，并验证了方法对候选点精确定位不敏感。在双足机器人上的实验表明，相较于基线方法，本方法在线速度估计中实现了优越的精度-效率权衡，并提升了滤波器一致性。这使得包括舞蹈与复杂地面交互在内的挑战性运动（仿真与真实环境均验证）得以鲁棒执行。