做足式机器人运动控制的团队终于有了一个能处理滑移和部分接触的状态估计方案——CoCo-InEKF 用学习协方差替代硬接触开关，实测支持跳舞等高动态动作，值得在真实机器人上试试。
足式机器人在高速动态运动中的状态估计仍具挑战，传统方法依赖二元接触状态，无法处理部分接触或方向性滑移。本文提出 CoCo-InEKF，一种可微不变扩展卡尔曼滤波器，用连续接触速度协方差替代二元接触状态，通过轻量神经网络端到端学习这些协方差，动态调整接触置信度。该方法无需启发式真值接触标签，并引入自动接触候选点选择流程。在双足机器人上的实验表明，CoCo-InEKF 在线性速度估计上实现了更优的精度-效率权衡，并改善了滤波器一致性，支持跳舞、复杂地面交互等挑战性运动。
CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios
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.