Coordinated crawling via reinforcement learning
Abstract
Rectilinear crawling locomotion is a primitive and common mode of locomotion in slender soft-bodied animals. It requires coordinated contractions that propagate along a body that interacts frictionally with its environment. We propose a simple approach to understand how this coordination arises in a neuromechanical model of a segmented, soft-bodied crawler via an iterative process that might have both biological antecedents and technological relevance. Using a simple reinforcement learning algorithm, we show that an initial all-to-all neural coupling converges to a simple nearest-neighbour neural wiring that allows the crawler to move forward using a localized wave of contraction that is qualitatively similar to what is observed in Drosophila melanogaster larvae and used in many biomimetic solutions. The resulting solution is a function of how we weight gait regularization in the reward, with a trade-off between speed and robustness to proprioceptive noise. Overall, our results, which embed the brain–body–environment triad in a learning scheme, have relevance for soft robotics while shedding light on the evolution and development of locomotion.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2020
- Source ID
- 10.1098/rsif.2020.0198
Entities
People
- Lakshminarayanan Mahadevan
- Shruti Mishra
- Wim M van Rees
Organizations
- Harvard University
- Massachusetts Institute of Technology
- United States Army Research Laboratory