Soft robots are naturally designed to perform safe interactions with their environment, like locomotion and manipulation. In the literature, there are now many concepts, often bio-inspired, to propose new modes of locomotion or grasping. However, a methodology for implementing motion planning of these tasks, as exists for rigid robots, is still lacking. One of the difficulties comes from the modeling of these robots, which is very different, as it is based on the mechanics of deformable bodies. These models, whose dimension is often very large, make learning and optimization methods very costly. In this paper, we propose a proxy approach, as exists for humanoid robotics. This proxy is a simplified model of the robot that enables frugal learning of a motion strategy. This strategy is then transferred to the complete model to obtain the corresponding actuation inputs. Our methodology is illustrated and analyzed on two classical designs of soft robots doing manipulation and locomotion tasks.

翻译：软体机器人被天然设计用于与环境进行安全交互，例如运动与操控。当前文献中已涌现大量概念（常受生物启发），用于提出新型运动或抓取模式。然而，针对这些任务的运动规划方法论——如同刚性机器人领域所建立的——仍存在缺失。其困难之一源于软体机器人建模的独特性：基于可变形体力学原理的模型通常维数极高，导致学习与优化方法成本高昂。本文提出一种仿人形机器人领域的代理方法。该代理作为机器人的简化模型，能实现运动策略的节俭式学习，随后将习得策略迁移至完整模型以获取对应驱动输入。我们通过两个分别执行操控与运动任务的经典软体机器人设计案例，对该方法论进行阐述与分析。