Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms

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.