The ability of a soft robot to perform specific tasks is determined by its contact configuration, and transitioning between configurations is often necessary to reach a desired position or manipulate an object. Based on this observation, we propose a method for controlling soft robots that involves defining a graph of configuration spaces. Different agents, whether learned or not (convex optimization, expert trajectory, and collision detection), use the structure of the graph to solve the desired task. The graph and the agents are part of the prior knowledge that is intuitively integrated into the learning process. They are used to combine different optimization methods, improve sample efficiency, and provide interpretability. We construct the graph based on the contact configurations and demonstrate its effectiveness through two scenarios, a deformable beam in contact with its environment and a soft manipulator, where it outperforms the baseline in terms of stability, learning speed, and interpretability.

翻译：软体机器人执行特定任务的能力由其接触配置决定，且通常需要在不同配置间进行转换才能达到目标位置或操作物体。基于这一观察，我们提出一种软体机器人控制方法：通过构建配置空间图结构。不同智能体（包括学习型与非学习型，如凸优化、专家轨迹、碰撞检测等）利用该图结构解决目标任务。该图结构及其所属智能体构成直观融入学习过程的先验知识，能够整合多种优化方法、提升样本效率并增强可解释性。我们基于接触配置构建该图结构，并通过两个场景（与环境接触的可变形梁及软体机械臂）验证其有效性，在稳定性、学习速度与可解释性方面均优于基线方法。