Terrain characterization and locomotion adaptation in a small-scale lizard-inspired robot

2026-03-06

AI Summary

This work addresses the challenge of deploying small-scale robots in complex natural terrains, where limited perception and adaptability often hinder performance. The authors propose SILA Bot, a lizard-inspired miniature robot that introduces a novel adaptive control framework with low computational overhead. By parameterizing body undulation patterns as a linear function of granular medium depth and relying solely on proprioceptive signals such as joint torque, the system enables real-time terrain identification and locomotion adaptation. A k-nearest neighbors classifier accurately estimates terrain depth with 95% accuracy, while a linear feedback controller dynamically adjusts body phase to autonomously switch gait patterns. This approach significantly enhances locomotion performance in unknown granular environments without requiring external sensing or high-level processing.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Unlike their large-scale counterparts, small-scale robots are largely confined to laboratory environments and are rarely deployed in real-world settings. As robot size decreases, robot-terrain interactions fundamentally change; however, there remains a lack of systematic understanding of what sensory information small-scale robots should acquire and how they should respond when traversing complex natural terrains. To address these challenges, we develop a Small-scale, Intelligent, Lizard-inspired, Adaptive Robot (SILA Bot) capable of adapting to diverse substrates. We use granular media of varying depths as a controlled yet representative terrain paradigm. We show that the optimal body movement pattern (ranging from standing-wave bending that assists limb retraction on flat ground to traveling-wave undulation that generates thrust in deep granular media) can be parameterized and approximated as a linear function of granular depth. Furthermore, proprioceptive signals, such as joint torque, provide sufficient information to estimate granular depth via a K-Nearest Neighbors classifier, achieving 95% accuracy. Leveraging these relationships, we design a simple linear feedback controller that modulates body phase and substantially improves locomotion performance on terrains with unknown depth. Together, these results establish a principled framework for perception and control in small-scale locomotion and enable effective terrain-adaptive locomotion while maintaining low computational complexity.

Problem

• small-scale robot
• terrain characterization
• locomotion adaptation
• robot-terrain interaction
• granular media

Innovation

• small-scale robot
• terrain adaptation
• proprioceptive sensing
• granular media locomotion
• linear feedback control