# Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain

## TL;DR

This paper presents a method for visually estimating surface friction using CNNs and integrating this information into hierarchical biped robot planning to enable autonomous movement on slippery terrains.

## Contribution

It introduces a novel approach combining visual friction prediction with hierarchical planning using chance constraints for improved robot locomotion.

## Key findings

- Effective friction prediction from real outdoor images.
- Successful autonomous navigation on slippery surfaces.
- Robust integration of friction uncertainty into planning.

## Abstract

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction.