RT Journal Article
SR Electronic
T1 Exploration-based learning of a step to step controller predicts locomotor adaptation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.18.435986
DO 10.1101/2021.03.18.435986
A1 Nidhi Seethapathi
A1 Barrett Clark
A1 Manoj Srinivasan
YR 2021
UL http://biorxiv.org/content/early/2021/03/19/2021.03.18.435986.abstract
AB Humans are able to adapt their locomotion to a variety of novel circumstances, for instance, walking on diverse terrain and walking with new footwear. During locomotor adaptation, humans have been shown to exhibit stereotypical changes in their movement patterns. Here, we provide a theoretical account of such locomotor adaptation, positing that the nervous system prioritizes stability in the short timescale and improves energy expenditure over a longer timescale. The resulting mathematical model has two processes: a stabilizing controller which is gradually changed by a reinforcement learner that exploits local gradients to lower energy expenditure, estimating gradients indirectly via intentional exploratory noise. We consider this model walking and adapting under three novel circumstances: walking on a split-belt treadmill (walking with each foot on a different belt, each belt at different speeds), walking with an exoskeleton, and walking with an asymmetric leg mass. This model predicts the short and long timescale changes observed in walking symmetry on the split-belt treadmill and while walking with the asymmetric mass. The model exhibits energy reductions with exoskeletal assistance, as well as entrainment to time-periodic assistance. We show that such exploration-based learning is degraded in the presence of large sensorimotor noise, providing a potential account for some impairments in learning.Competing Interest StatementThe authors have declared no competing interest.