Soft continuum arms (SCAs) are increasingly recognized for their ability to safely and effectively interact with complex, unstructured environments. Their ability to conform and apply gentle forces makes them ideal for tasks such as handling delicate objects or working in close proximity to humans [Chen et al., 2022, Zongxing et al., 2020, Banerjee et al., 2018, Chen et al., 2021, V enter and Dirven, 2017]. However, their soft and deformable nature introduces challenges for modeling and control. Learning-enabled methods, such as model-free reinforcement learning (RL), offer a promising solution by learning behaviors directly from data rather than relying on analytically derived models [Falotico et al., 2024]. Despite these advantages, one of the primary obstacles to deploying SCAs in real-world is the sim-to-real transfer, where policies trained in simulation fail to generalize well on physical systems.

We present a system for non-prehensile manipulation that require a significant number of contact mode transitions and the use of environmental contacts to successfully manipulate an object to a target location. Our method is based on deep reinforcement learning which, unlike state-of-the-art planning algorithms, does not require apriori knowledge of the physical parameters of the object or environment such as friction coefficients or centers of mass. The planning time is reduced to the simple feed-forward prediction time on a neural network. We propose a computational structure, action space design, and curriculum learning scheme that facilitates efficient exploration and sim-to-real transfer. In challenging real-world non-prehensile manipulation tasks, we show that our method can generalize over different objects, and succeed even for novel objects not seen during training. Project website: https://sites.google.com/view/nonprenehsile-decomposition