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Soft robots are naturally designed to perform safe interactions with theirenvironment, like locomotion and manipulation. In the literature, there are nowmany concepts, often bio-inspired, to propose new modes of locomotion orgrasping. However, a methodology for implementing motion planning of thesetasks, as exists for rigid robots, is still lacking. One of the difficultiescomes from the modeling of these robots, which is very different, as it isbased on the mechanics of deformable bodies. These models, whose dimension isoften very large, make learning and optimization methods very costly. In thispaper, we propose a proxy approach, as exists for humanoid robotics. This proxyis a simplified model of the robot that enables frugal learning of a motionstrategy. This strategy is then transferred to the complete model to obtain thecorresponding actuation inputs. Our methodology is illustrated and analyzed ontwo classical designs of soft robots doing manipulation and locomotion tasks.

Toward the use of proxies for efficient learning manipulation and locomotion strategies on soft robots