Stand, Walk, Navigate: Recovery-Aware Visual Navigation on a Low-Cost Wheeled Quadruped

TL;DR

This paper introduces a low-cost, recovery-aware visual-inertial navigation system for wheeled quadruped robots, enabling robust autonomous movement and fall recovery across diverse terrains using vision and deep reinforcement learning.

Contribution

It presents a novel integration of vision-based perception and deep reinforcement learning for robust navigation and recovery in low-cost wheeled quadruped robots, addressing a gap in fall recovery capabilities.

Findings

Successful simulation of agile mobility on irregular terrain.

Reliable recovery from external perturbations and failures.

Effective goal-directed navigation in indoor environments.

Abstract

Wheeled-legged robots combine the efficiency of wheels with the obstacle negotiation of legs, yet many state-of-the-art systems rely on costly actuators and sensors, and fall-recovery is seldom integrated, especially for wheeled-legged morphologies. This work presents a recovery-aware visual-inertial navigation system on a low-cost wheeled quadruped. The proposed system leverages vision-based perception from a depth camera and deep reinforcement learning policies for robust locomotion and autonomous recovery from falls across diverse terrains. Simulation experiments show agile mobility with low-torque actuators over irregular terrain and reliably recover from external perturbations and self-induced failures. We further show goal directed navigation in structured indoor spaces with low-cost perception. Overall, this approach lowers the barrier to deploying autonomous navigation and robust locomotion policies in budget-constrained robotic platforms.