CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios

Engineering notes

CoCo-InEKF can significantly enhance the performance of legged robots in complex motions like dancing and interacting with diverse terrains. Its insensitivity to contact candidate placement simplifies real-world applications.

Chinese explanation / 中文解读

本文提出了一种新型的状态估计算法CoCo-InEKF，适用于动态接触丰富的场景。该方法通过学习连续接触速度协方差，动态调整接触信心，解决了传统方法中无法处理部分接触和方向滑移的问题。实验表明，该算法在双足机器人运动中的精度和效率优于基线方法。

Original abstract

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.

Links and sources

• Official / arXiv page
• PDF from original source

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