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：在不确定性环境下的机器人抓取因其接触丰富且高度不确定的特性，始终是一项基础性挑战。传统刚性机械手受限于自由度和柔顺性，需依赖复杂的基于模型的方法和重型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的机械智能：其欠驱动结构及全身被动柔顺性能够自然地适应不确定接触并实现自适应行为。为充分利用这一能力，我们提出了一种轻量级驱动空间学习框架——通过流匹配模型（修正流），直接从确定性演示中推断出全身软体机器人抓取的分布性控制表征，无需密集传感或重型控制回路。仅使用30组演示（覆盖不足8%的可达工作空间），学习策略即可在整个工作空间内实现97.5%的抓取成功率，泛化至±33%的抓取目标尺寸变化，并在将执行时间缩放至20%~200%以直接调整机器人动态响应时保持性能稳定。这些结果表明，通过利用被动冗余自由度与柔顺性，驱动空间学习可将本体机械结构转化为功能性控制智能，从而为该高不确定性任务显著减轻中央控制器的负担。