A Simplified Underactuated Platform for AI-Ready Bipedal Walking Control: The Stilt-bot

Abstract

Recent advancements in applying machine learning to bipedal robots have demonstrated significant potential. However, the need for more comprehensive Artificial Intelligence (AI) testing environments has become increasingly evident, as current evaluations are constrained by limited datasets, making real-world testing essential. This paper presents Stilt-bot, an AI test bed specifically designed to support skill transfer across various robotic platforms, regardless of changes in size, shape, or actuator power. Inspired by how humans adapt their gait throughout growth, Stilt-bot employs a simple yet versatile 6 degrees-of-freedom (DOF) design and a prismatic sliding mechanism that enhance its agility and reduce weight. This configuration allows easy modifications to its height, mass, and power, providing a flexible and intuitive platform for evaluating AI-based walking control strategies. Both simulation and experimental results confirm Stilt-bot’s capability for stable flat-ground walking, demonstrating its effectiveness as a test bed for developing robust AI-driven bipedal locomotion.