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An energy landscape approach to locomotor transitions in complex 3D terrain

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Document pages: 55 pages

Abstract: Effective locomotion in nature happens by transitioning across multiple modes(e.g., walk, run, climb). Despite this, far more mechanistic understanding ofterrestrial locomotion has been on how to generate and stabilize aroundnear-steady-state movement in a single mode. We still know little about howlocomotor transitions emerge from physical interaction with complex terrain.Consequently, robots largely rely on geometric maps to avoid obstacles, nottraverse them. Recent studies revealed that locomotor transitions in complex3-D terrain occur probabilistically via multiple pathways. Here, we show thatan energy landscape approach elucidates the underlying physical principles. Wediscovered that locomotor transitions of animals and robots self-propelledthrough complex 3-D terrain correspond to barrier-crossing transitions on apotential energy landscape. Locomotor modes are attracted to landscape basinsseparated by potential energy barriers. Kinetic energy fluctuation fromoscillatory self-propulsion helps the system stochastically escape from onebasin and reach another to make transitions. Escape is more likely towardslower barrier direction. These principles are surprisingly similar to those ofnear-equilibrium, microscopic systems. Analogous to free energy landscapes formulti-pathway protein folding transitions, our energy landscape approach fromfirst principles is the beginning of a statistical physics theory ofmulti-pathway locomotor transitions in complex terrain. This will not only helpunderstand how the organization of animal behavior emerges from multi-scaleinteractions between their neural and mechanical systems and the physicalenvironment, but also guide robot design, control, and planning over the large,intractable locomotor-terrain parameter space to generate robust locomotortransitions through the real world.

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