“An energy landscape approach to locomotor transitions in complex terrain”
Presented by Professor Chen Li
Department of Mechanical Engineering, Johns Hopkins University
Effective locomotion in nature happens by transitioning between multiple modes (e.g., walk, run, slither, climb, fly, swim, jump, burrow). Despite this, far more of our mechanistic understanding of terrestrial locomotion has been on understanding how to generate and stabilize near-steady-state, limit-cycle-like movement using a single mode.
In this talk, I will give an overview of my lab’s research to begin to fill this knowledge gap. We discovered that an energy landscape approach helps understand how stochastic, multi-pathway transitions across locomotor modes statistically emerge from physical interaction with complex terrain. Animals’ and robots’ locomotor modes are attracted to basins of a potential energy landscape, and they can use a suite of strategies to escape from one basin and find another, thereby generating locomotor transitions.
Our energy landscape approach provided a new conceptual way of thinking about terrestrial locomotion beyond limit cycles. We envision our energy landscapes as the beginning of a statistical physics theory that will quantitatively predict global structures and emergent dynamics of locomotor transitions, which will be useful for the design, control, and planning of robots moving through the real world.
Chen Li is an Assistant Professor in the Department of Mechanical Engineering at Johns Hopkins University, and affiliated with JHU’s Laboratory for Computational Sensing and Robotics (LCSR). Dr. Li received his B.S. degree from Peking University in 2005 and Ph.D. degree from Georgia Institute of Technology in 2011, both in physics. From 2012 to 2015, he performed postdoctoral research in Integrative Biology and Robotics at University of California, Berkeley. Dr. Li is recipient of a Miller Research Fellowship from University of California, Berkeley in 2012, a Burroughs Wellcome Fund Career Award at the Scientific Interface in 2015, an Army Research Office Young Investigator Award in 2017, and a Beckman Young Investigator Award in 2018. His research achievements have been recognized by publication in journals including Science and PNAS, as well as selection for one best paper (Advanced Robotics 2017), two highlight papers (IROS 2016, Bioinspiration & Biomimetics 2015), and two best student papers (SICB 2009, RSS 2012). He has also been selected as an alumnus of the Kavli Frontiers of Science of National Academy of Sciences.
To learn more, visit Terradynamics Lab at: https://li.me.jhu.edu/