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Metastable Walking on Stochastically Rough Terrain

Abstract

Simplified models of limit-cycle walking on flat terrain have provided important insights into the nature of legged locomotion. Real walking robots (and humans), however, do not exhibit true limit cycle dynamics because terrain, even in a carefully designed laboratory setting, is inevitably non-flat. % also mention sensor noise and disturbances? Walking systems on stochastically rough terrain may not satisfy strict conditions for limit-cycle stability, but can still demonstrate impressively long-living periods of continuous walking. Here, we examine the dynamics of rimless-wheel and compass-gait walking on randomly generated rough terrain, and employ tools from stochastic processes to describe the `stochastic stability' of these gaits. This analysis generalizes our understanding of walking stability, and may provide statistical tools for experimental limit cycle analysis on real walking systems.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer — walking dynamics
🐣 Hot Topic Early Bird — stochastic process
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio