# Probabilistic Contact State Estimation for Legged Robots using Inertial Information

## TL;DR

This paper presents a probabilistic contact state estimation method for legged robots using only IMU data, enabling reliable contact detection in unstructured terrains without force sensors.

## Contribution

It introduces a novel IMU-based probabilistic approach for contact state estimation that relies solely on proprioceptive sensing, unlike existing methods requiring force or kinematic data.

## Key findings

- Effective in real and simulated scenarios
- Applicable to bipeds and quadrupeds
- Outperforms traditional methods in uncertain terrains

## Abstract

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.