Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network
This work addresses the challenge of domain adaptation for soft robotic controllers, enabling more adaptable systems as materials degrade over time, but it is incremental as it builds on existing GAN-based methods for a specific robotics application.
The paper tackled the problem of transferring learned dynamics across domains with different physical properties in soft robotics, using a conditional cycle generative adversarial network (CCGAN) to adapt a pose controller from a standard simulation to a domain with tenfold increased viscosity, achieving effective cross-domain skill transfer as demonstrated in trajectory-tracking experiments.
Deep learning provides a powerful method for modeling the dynamics of soft robots, offering advantages over traditional analytical approaches that require precise knowledge of the robot's structure, material properties, and other physical characteristics. Given the inherent complexity and non-linearity of these systems, extracting such details can be challenging. The mappings learned in one domain cannot be directly transferred to another domain with different physical properties. This challenge is particularly relevant for soft robots, as their materials gradually degrade over time. In this paper, we introduce a domain translation framework based on a conditional cycle generative adversarial network (CCGAN) to enable knowledge transfer from a source domain to a target domain. Specifically, we employ a dynamic learning approach to adapt a pose controller trained in a standard simulation environment to a domain with tenfold increased viscosity. Our model learns from input pressure signals conditioned on corresponding end-effector positions and orientations in both domains. We evaluate our approach through trajectory-tracking experiments across five distinct shapes and further assess its robustness under noise perturbations and periodicity tests. The results demonstrate that CCGAN-GP effectively facilitates cross-domain skill transfer, paving the way for more adaptable and generalizable soft robotic controllers.