Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control

TL;DR

This paper presents a seeing-eye quadruped robot system that uses reinforcement learning and supervised learning to navigate while responding to external human-applied forces, aiding visually impaired users.

Contribution

It introduces a novel combined RL and supervised learning approach to enable a quadruped robot to handle external tugging forces during navigation.

Findings

The controller is robust to external forces in simulation and hardware.

The force estimator accurately detects force direction.

The system successfully guides a blindfolded human in real-world tests.

Abstract

Seeing-eye robots are very useful tools for guiding visually impaired people, potentially producing a huge societal impact given the low availability and high cost of real guide dogs. Although a few seeing-eye robot systems have already been demonstrated, none considered external tugs from humans, which frequently occur in a real guide dog setting. In this paper, we simultaneously train a locomotion controller that is robust to external tugging forces via Reinforcement Learning (RL), and an external force estimator via supervised learning. The controller ensures stable walking, and the force estimator enables the robot to respond to the external forces from the human. These forces are used to guide the robot to the global goal, which is unknown to the robot, while the robot guides the human around nearby obstacles via a local planner. Experimental results in simulation and on hardware show that our controller is robust to external forces, and our seeing-eye system can accurately detect force direction. We demonstrate our full seeing-eye robot system on a real quadruped robot with a blindfolded human. The video can be seen at our project page: https://bu-air-lab.github.io/guide_dog/