From Impact to Insight: Dynamics-Aware Proprioceptive Terrain Sensing on Granular Media

• URL: http://arxiv.org/abs/2604.02563v1
• Date: Thu, 02 Apr 2026 22:27:18 GMT
• Title: From Impact to Insight: Dynamics-Aware Proprioceptive Terrain Sensing on Granular Media
• Abstract summary: Most terrain characterization approaches rely on quasi-static assumptions.<n>We develop a physics-based framework for dynamic terrain characterization using proprioceptive sensing alone.
• Score: 8.664271824657208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots that traverse natural terrain must interpret contact forces generated under highly dynamic conditions. However, most terrain characterization approaches rely on quasi-static assumptions that neglect velocity- and acceleration-dependent effects arising during impact and rapid stance transitions. In this work, we investigate granular terrain interaction during high-speed hopping and develop a physics-based framework for dynamic terrain characterization using proprioceptive sensing alone. Through controlled hopping experiments with systematically varied impact speed and leg compliance, our measurements reveal that quasi-static based assumptions lead to large discrepancies in granular terrain property estimation during high-speed hopping, particularly upon touchdown and controller-induced stiffness transitions. Velocity-dependent drag alone cannot explain these discrepancies. Instead, acceleration-dependent added-mass effects-associated with grain entrainment beneath the foot-dominate transient force responses. We integrate this force decomposition with a momentum-observer-based estimator that compensates for rigid-body inertia and gravity, and introduce an acceleration-aware weighted regression to account for increased force variance during high-acceleration events. Together, these methods enable consistent recovery of granular stiffness parameters across locomotion conditions, closely matching linear-actuator ground truth. Our results demonstrate that accurate terrain inference during high-speed locomotion requires explicit treatment of acceleration-dependent granular effects, and provide a foundation for robots to characterize complex deformable terrain during dynamic exploration of terrestrial and planetary environments.

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