Fear-Based Behavior Adaptation for Robust Walking Robots using Unsupervised Health Estimation

Abstract

Mobile robots can perform increasingly impressive feats in controlled environments. Many real applications, though, especially for walking robots, introduce a high degree of unforeseen difficulties, yet require very robust robot operation. In these cases, it is still often not possible to guarantee the needed reliability.We present an approach to utilize unsupervised anomaly detection to implement a fear-based adaptation of robot behavior. This allows robots to automatically and quickly react to any type of unexpected problems. Neither the environment nor the type of disturbance has to be known beforehand, as the system requires only a small amount of baseline data for training, which can be collected in a laboratory environment. Additionally, it can work on arbitrary robot hardware and be integrated in all types of robot control structures.We evaluated our approach in simulation and on state of the art walking robots, ANYmal, Spot and our own six-legged walking robot prototype, in a realistic field test environment in the Tabernas desert in Spain. Our results showcase that we can quickly detect arbitrary problems based on significantly different types of sensor data and decrease robot fall rates in the most extreme scenarios from 56% to 4%. This promises significant increases in robustness for all types of walking robots in highly challenging and previously unknown environments.