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

arXiv: https://arxiv.org/abs/2504.16916 · HTML: https://mlsanity.com/paper/2504.16916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment](https://mlsanity.com/paper/2202.05200/) (2022)
- [Cosserat-Rod Based Dynamic Modeling of Soft Slender Robot Interacting with Environment](https://mlsanity.com/paper/2307.06261/) (2023)

## Built on by (most cited first, 1 of 1)

- [Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation](https://mlsanity.com/paper/2505.16547/) (2025)

## Abstract (arXiv)

Soft continuum arms (SCAs) soft and deformable nature presents challenges in modeling and control due to their infinite degrees of freedom and non-linear behavior. This work introduces a reinforcement learning (RL)-based framework for visual servoing tasks on SCAs with zero-shot sim-to-real transfer capabilities, demonstrated on a single section pneumatic manipulator capable of bending and twisting. The framework decouples kinematics from mechanical properties using an RL kinematic controller for motion planning and a local controller for actuation refinement, leveraging minimal sensing with visual feedback. Trained entirely in simulation, the RL controller achieved a 99.8% success rate. When deployed on hardware, it achieved a 67% success rate in zero-shot sim-to-real transfer, demonstrating robustness and adaptability. This approach offers a scalable solution for SCAs in 3D visual servoing, with potential for further refinement and expanded applications.