Machine Learning for Robot Locomotion in Flowable Materials

Recent studies of robot movement in flowable granular media (inspired by difficulties faced by extraterrestrial rovers) reveal a coupled locomotor/substrate effect (Shrivastava et al., Sci. Rob. 2020) where the robot spontaneously remodels its environment; this strong coupling occurs in certain limb/wheel movement patterns and results in a localized granular flow allowing the robot to effectively “swim” up highly flowable slopes. However, these gaits were discovered via trial and error by human operators. To accelerate the discovery of effective gaits in flowable frictional media, we use a neural net-based machine learning (ML) scheme to characterize the gait and terrain interactions. We capture the robot kinematics and its surrounding terrain deformation using external depth cameras and train an ML model to describe the coupling of the robot/terrain system. The ML approach for the substrate flow offers an approximate numerical model of the environment that learns from terrain data, circumventing the need for computationally costly continuum models for frictional material. Our scheme may improve robot mobility in situ for real-world environments by offering adaptability to flowable terrain via rapid learning.