Robotic grasping under uncertainty remains a fundamental challenge due to its uncertain and contact-rich nature. Traditional rigid robotic hands, with limited degrees of freedom and compliance, rely on complex model-based and heavy feedback controllers to manage such interactions. Soft robots, by contrast, exhibit embodied mechanical intelligence: their underactuated structures and passive flexibility of their whole body, naturally accommodate uncertain contacts and enable adaptive behaviors. To harness this capability, we propose a lightweight actuation-space learning framework that infers distributional control representations for whole-body soft robotic grasping, directly from deterministic demonstrations using a flow matching model (Rectified Flow),without requiring dense sensing or heavy control loops. Using only 30 demonstrations (less than 8% of the reachable workspace), the learned policy achieves a 97.5% grasp success rate across the whole workspace, generalizes to grasped-object size variations of +-33%, and maintains stable performance when the robot's dynamic response is directly adjusted by scaling the execution time from 20% to 200%. These results demonstrate that actuation-space learning, by leveraging its passive redundant DOFs and flexibility, converts the body's mechanics into functional control intelligence and substantially reduces the burden on central controllers for this uncertain-rich task.

翻译：在不确定性条件下的机器人抓取因其不确定性和接触密集特性，仍然是一个基础性挑战。传统刚性机械手由于自由度有限且顺应性不足，需依赖复杂的基于模型的重反馈控制器来管理此类交互。相比之下，软体机器人展现出具身机械智能：其欠驱动结构和全身被动柔顺性能够自然适应不确定接触并实现自适应行为。为利用这一能力，我们提出了一种轻量化的驱动空间学习框架，通过流匹配模型（Rectified Flow）直接从确定性演示中推断全身软体机器人抓取的概率分布控制表示，无需密集传感或重型控制回路。仅使用30次演示（少于可达工作空间的8%），学习策略在整个工作空间内实现了97.5%的抓取成功率，对抓取物体尺寸变化±33%具有泛化能力，并在机器人动态响应通过缩放执行时间（20%至200%）直接调整时保持稳定性能。这些结果表明，驱动空间学习通过利用其被动冗余自由度和柔顺性，将身体力学转化为功能性控制智能，并显著减轻了中央控制器在这一高度不确定任务中的负担。