  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.
