My research aims at understanding the biomechanics of animal locomotion and explaining why/how they have evolved the form they take. Specifically, I focus on “mesoscale locomotion,” a regime where the size and weight of the locomotor are comparable to those of the terrain substrates (e.g., leaf litter, debris, and soil). Mesoscale locomotion is characterized by highly coupled locomotor-substrate interactions, resulting in a noise-dominated complex system for which there is a lack of analytical/numerical/experimental models. Despite the complexity, mesoscale habitats are home to a rich diversity of organisms, but the absence of quantitative models limits our understanding of both animal behaviors and their evolutionary pathways.

To address this gap, my current research has established theoretical frameworks (e.g., geometric phase approach [Rieser* and Chong* et al., 2024, PNAS] and stochastic transportation [Chong et al., 2023, Science]) to model mesoscale locomotion. Building on these foundations, my future work will develop these theories into practical computational/analytical tools to hypothesize on (1) how animals coordinate their behavioral strategies to detect and respond to terrain uncertainties at the mesoscale and (2) how morphological variations affect the complexity of these strategies. These hypotheses will be validated through both biological and robotic experiments. For biological experiments, I will use reptiles and centipedes as model organisms, featuring their characteristic diversity in morphology and easily tractable kinematics [Chong et al., 2022 & 2023 PNAS]. Additionally, robot experiments, with programmable morphology and movement, will be used to simulate and explore infrequent or extinct animal behaviors, offering a novel way to cross-validate theoretical models and expand our understanding of biomechanics across evolutionary timescales.