Legged robot navigation in unstructured and slippery terrains depends heavily on the ability to accurately identify the quality of contact between the robot's feet and the ground. Contact state estimation is regarded as a challenging problem and is typically addressed by exploiting force measurements, joint encoders and/or robot kinematics and dynamics. In contrast to most state of the art approaches, the current work introduces a novel probabilistic method for estimating the contact state based solely on proprioceptive sensing, as it is readily available by Inertial Measurement Units (IMUs) mounted on the robot's end effectors. Capitalizing on the uncertainty of IMU measurements, our method estimates the probability of stable contact. This is accomplished by approximating the multimodal probability density function over a batch of data points for each axis of the IMU with Kernel Density Estimation. The proposed method has been extensively assessed against both real and simulated scenarios on bipedal and quadrupedal robotic platforms such as ATLAS, TALOS and Unitree's GO1.

翻译：腿式机器人在非结构化和光滑地形中的导航高度依赖于准确识别机器人足部与地面接触质量的能力。接触状态估计被认为是一个具有挑战性的问题，通常通过利用力测量、关节编码器和/或机器人运动学与动力学来解决。与大多数现有方法不同，本研究提出了一种新颖的概率方法，仅基于本体感知（即机器人末端执行器上安装的惯性测量单元提供的即时可用数据）来估计接触状态。利用IMU测量的不确定性，我们的方法估计稳定接触的概率。这是通过使用核密度估计，对IMU每个轴上的一批数据点近似多峰概率密度函数来实现的。该方法已在ATLAS、TALOS和宇树科技GO1等双足和四足机器人平台上，针对真实和模拟场景进行了广泛评估。