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

• URL: http://arxiv.org/abs/2603.05837v1
• Date: Fri, 06 Mar 2026 02:44:59 GMT
• Title: Terrain characterization and locomotion adaptation in a small-scale lizard-inspired robot
• Abstract summary: We develop a Small-scale, Intelligent, Lizard-inspired, Adaptive Robot (SILA Bot) capable of adapting to diverse substrates.<n>We show that the optimal body movement pattern can be parameterized and approximated as a linear function of granular depth.<n>We also design a simple linear feedback controller that modulates body phase and substantially improves performance on terrains with unknown depth.
• Score: 0.2770822269241973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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